Journal articles: 'Landmark Estimation'

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Sakai, Atsushi, Teppei Saitoh, and Yoji Kuroda. "Robust Landmark Estimation and Unscented Particle Sampling for SLAM in Dynamic Outdoor Environment." Journal of Robotics and Mechatronics 22, no. 2 (April 20, 2010): 140–49. http://dx.doi.org/10.20965/jrm.2010.p0140.

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In this paper, we propose a set of techniques for accurate and practical Simultaneous Localization And Mapping (SLAM) in dynamic outdoor environments. The techniques are categorized into Landmark estimation and Unscented particle sampling. Landmark estimation features stable feature detection and data management for estimating landmarks accurately, robustly, and at a low-calculation cost. The stable feature detection removes dynamic objects and sensor noise with scan subtraction, detects feature points sparsely and evenly, and sets data association parameters with landmark density. The data management calculates landmark existence probability and spurious landmarks are removed, utilizes landmark exclusivity for data association, and predicts importance weights using the observation range. Unscented particle sampling is based on Unscented Transformation for accurate SLAM. Simulation results of SLAM using our landmark estimation and experimental results of our SLAM in dynamic outdoor environments are presented and discussed. The results show that our landmark estimation decrease SLAM calculation time and maximum position error by 80% compared to conventional landmark estimation, and position estimation of SLAM with Unscented particle sampling ismore accurate than FastSLAM2.0 in dynamic outdoor environments.

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Liu, Shengli, Xiaowen Zhu, Zewei Cao, and Gang Wang. "Deep 1D Landmark Representation Learning for Space Target Pose Estimation." Remote Sensing 14, no. 16 (August 18, 2022): 4035. http://dx.doi.org/10.3390/rs14164035.

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Monocular vision-based pose estimation for known uncooperative space targets plays an increasingly important role in on-orbit operations. The existing state-of-the-art methods of space target pose estimation build the 2D-3D correspondences to recover the space target pose, where space target landmark regression is a key component of the methods. The 2D heatmap representation is the dominant descriptor in landmark regression. However, its quantization error grows dramatically under low-resolution input conditions, and extra post-processing is usually needed to compute the accurate 2D pixel coordinates of landmarks from heatmaps. To overcome the aforementioned problems, we propose a novel 1D landmark representation that encodes the horizontal and vertical pixel coordinates of a landmark as two independent 1D vectors. Furthermore, we also propose a space target landmark regression network to regress the locations of landmarks in the image using 1D landmark representations. Comprehensive experiments conducted on the SPEED dataset show that the proposed 1D landmark representation helps the proposed space target landmark regression network outperform existing state-of-the-art methods at various input resolutions, especially at low resolutions. Based on the 2D landmarks predicted by the proposed space target landmark regression network, the error of space target pose estimation is also smaller than existing state-of-the-art methods under all input resolution conditions.

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Fujii, Hajime, Yoshinobu Ando, Takashi Yoshimi, and Makoto Mizukawa. "Shape Recognition of Metallic Landmark and its Application to Self-Position Estimation for Mobile Robot." Journal of Robotics and Mechatronics 22, no. 6 (December 20, 2010): 718–25. http://dx.doi.org/10.20965/jrm.2010.p0718.

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This paper proposes a method of improving selfposition estimation accuracy with metallic landmarks for mobile robots. Many methods of the past selfposition estimation researches have used GPS, laserrange scanners, and CCD cameras, but have been unable to obtain landmark information correctly due to environmental factors. Metallic landmarks are useful in environments where conventional sensors do not work well. Self-position estimation accuracy is thus increased by combining metallic landmark information with that from other equipment.

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Floreskul, Volodymyr, Konstantin Tretyakov, and Marlon Dumas. "Memory-Efficient Fast Shortest Path Estimation in Large Social Networks." Proceedings of the International AAAI Conference on Web and Social Media 8, no. 1 (May 16, 2014): 91–100. http://dx.doi.org/10.1609/icwsm.v8i1.14532.

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As the sizes of contemporary social networks surpass billions of users, so grows the need for fast graph algorithms to analyze them. A particularly important basic operation is the computation of shortest paths between nodes. Classical exact algorithms for this problem are prohibitively slow on large graphs, which motivates the development of approximate methods. Of those, landmark-based methods have been actively studied in recent years. Landmark-based estimation methods start by picking a fixed set of landmark nodes, precomputing the distance from each node in the graph to each landmark, and storing the precomputed distances in a data structure. Prior work has shown that the number of landmarks required to achieve a given level of precision grows with the size of the graph. Simultaneously, the size of the data structure is proportional to the product of the size of the graph and the number of landmarks. In this work we propose an alternative landmark-based distance estimation approach that substantially reduces space requirements by means of pruning: computing distances from each node to only a small subset of the closest landmarks. We evaluate our method on the DBLP, Orkut, Twitter and Skype social networks and demonstrate that the resulting estimation algorithms are comparable in query time and potentially superior in approximation quality to equivalent non-pruned landmark-based methods, while requiring less memory or disk space.

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Fong, Li Wei, Pi Ching Lou, and Ke Jia Tang. "Vehicle Kinematic State Estimation Using Passive Sensor Fusion Approach." Applied Mechanics and Materials 271-272 (December 2012): 1709–12. http://dx.doi.org/10.4028/www.scientific.net/amm.271-272.1709.

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The main issue addressed here is that of estimating the kinematic state components of a vehicle in autonomous navigation using landmark angle-only measurements from an onboard passive sensor. The estimates of the absolute position and velocity of the vehicle are provided by a hybrid coordinate fusion filter. The hierarchical architecture of the filter which consists of a group of local processors and a global processor is developed for improving estimation accuracy. In each local processor, an extended Kalman filter uses hybrid information from the reference Cartesian coordinate system and the modified polar coordinate system for state and state error covariance extrapolation and updating. In the global processor, a weighted least squares estimator is utilized to combine the outputs of local processors to form a global estimate. By using only two landmarks simulation results show that proposed algorithm improves the estimation accuracy drastically.

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Mohd Shah, Hairol Nizam, Zalina Kamis, Azhar Ahmad, Mohd Rizuan Baharon, Muhd Akmal Noor Rajikon, and Kang Hui Hwa. "Vision Based Position Control for Vertical Take-off and Landing (VTOL) Using One Singular Landmark." Modern Applied Science 13, no. 9 (August 22, 2019): 33. http://dx.doi.org/10.5539/mas.v13n9p33.

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This project presents a vision based position control for Vertical Take-off and Landing (VTOL) to recognise a singular landmark for landing and take-off. Position control can provide safe flight and an accurate navigation. The circle landmark which used is an artificial landmark at known locations in an environment. Initially, a camera mounted on VTOL facing downward detecting landmarks in environments. A single circle used as landmark and VTOL will be control the position to reach the landmark. The images from the down-looking camera provided vision data to estimates position of VTOL from landmark. A mathematical method based on projective geometry using to locate VTOL on desired landmark from projected point in capture image. By compute the x-y coordinates of the VTOL with respect to landmark, height of camera above landmark will be obtained. VTOL can localize itself in known environment with pose estimation from landmark. The graphic user interface system (GUI) generate by MATLAB software is used to communicate with VTOL to control the VTOL position

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Chen, Yao Chang, Ta Ming Shih, and Chung Ho Wang. "Stereo Vision Specific Observation Model for EKF-Based SLAM." Applied Mechanics and Materials 373-375 (August 2013): 238–41. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.238.

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This work addresses a new probabilistic observation model for a stereo simultaneous localization and mapping (SLAM) system within the standard Extended-Kalman filter (EKF) framework. The observation modal was derived by using the inverse depth parameterization as the landmark modal, and contributes to both bearing and range information into the EKF estimation. In this way the inherently non-linear problem cause by the projection equations is resolved and real depth uncertainty distribution of landmarks features can be accurately estimated. The system was demonstrated with real-world outdoor data. Analysis results show landmark feature depth estimation is more stable and the uncertainty noise converges faster than the traditional approach.

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Hsu, Chen-Chien, Cheng-Kai Yang, Yi-Hsing Chien, Yin-Tien Wang, Wei-Yen Wang, and Chiang-Heng Chien. "Computationally efficient algorithm for vision-based simultaneous localization and mapping of mobile robots." Engineering Computations 34, no. 4 (June 12, 2017): 1217–39. http://dx.doi.org/10.1108/ec-05-2015-0123.

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Purpose FastSLAM is a popular method to solve the problem of simultaneous localization and mapping (SLAM). However, when the number of landmarks present in real environments increases, there are excessive comparisons of the measurement with all the existing landmarks in each particle. As a result, the execution speed will be too slow to achieve the objective of real-time navigation. Thus, this paper aims to improve the computational efficiency and estimation accuracy of conventional SLAM algorithms. Design/methodology/approach As an attempt to solve this problem, this paper presents a computationally efficient SLAM (CESLAM) algorithm, where odometer information is considered for updating the robot’s pose in particles. When a measurement has a maximum likelihood with the known landmark in the particle, the particle state is updated before updating the landmark estimates. Findings Simulation results show that the proposed CESLAM can overcome the problem of heavy computational burden while improving the accuracy of localization and mapping building. To practically evaluate the performance of the proposed method, a Pioneer 3-DX robot with a Kinect sensor is used to develop an RGB-D-based computationally efficient visual SLAM (CEVSLAM) based on Speeded-Up Robust Features (SURF). Experimental results confirm that the proposed CEVSLAM system is capable of successfully estimating the robot pose and building the map with satisfactory accuracy. Originality/value The proposed CESLAM algorithm overcomes the problem of the time-consuming process because of unnecessary comparisons in existing FastSLAM algorithms. Simulations show that accuracy of robot pose and landmark estimation is greatly improved by the CESLAM. Combining CESLAM and SURF, the authors establish a CEVSLAM to significantly improve the estimation accuracy and computational efficiency. Practical experiments by using a Kinect visual sensor show that the variance and average error by using the proposed CEVSLAM are smaller than those by using the other visual SLAM algorithms.

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D’Amelio, Richard, and Thomas J. Dunn. "Revisiting the Santa Barbara sense of direction scale, mental rotations, and gender differences in spatial orientation." PsyPag Quarterly 1, no. 115 (June 2020): 7–10. http://dx.doi.org/10.53841/bpspag.2020.1.115.7.

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Angular direction estimation to landmarks of varying distance in the physical environment was utilised to investigate the ecological validity of the Santa Barbara sense of direction scale (SBSOD). Two- and three-dimensional MR measures were included to enable further the scale applicability. Results showed a moderate correlation between SBSOD and angular deviation from landmarks in the immediate landscape, but not with local or distant landmarks. Moreover, the findings suggest that skills which underlie three-dimensional MR better relate to pointing accuracy (PA) of distant landmarks and the cardinal direction, North. Results also showed a gender-related systematic biases in landmark estimation.

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Chen, Haiwen, Jin Chen, Zhuohuai Guan, Yaoming Li, Kai Cheng, and Zhihong Cui. "Stereovision-Based Ego-Motion Estimation for Combine Harvesters." Sensors 22, no. 17 (August 25, 2022): 6394. http://dx.doi.org/10.3390/s22176394.

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Ego-motion estimation is a foundational capability for autonomous combine harvesters, supporting high-level functions such as navigation and harvesting. This paper presents a novel approach for estimating the motion of a combine harvester from a sequence of stereo images. The proposed method starts with tracking a set of 3D landmarks which are triangulated from stereo-matched features. Six Degree of Freedom (DoF) ego motion is obtained by minimizing the reprojection error of those landmarks on the current frame. Then, local bundle adjustment is performed to refine structure (i.e., landmark positions) and motion (i.e., keyframe poses) jointly in a sliding window. Both processes are encapsulated into a two-threaded architecture to achieve real-time performance. Our method utilizes a stereo camera, which enables estimation at true scale and easy startup of the system. Quantitative tests were performed on real agricultural scene data, comprising several different working paths, in terms of estimating accuracy and real-time performance. The experimental results demonstrated that our proposed perception system achieved favorable accuracy, outputting the pose at 10 Hz, which is sufficient for online ego-motion estimation for combine harvesters.

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Elli, Stefano, Giacomo Bellani, Luigi Cannizzo, Luciano Giannini, Christian De Felippis, Simona Vimercati, Fabiana Madotto, and Alberto Lucchini. "Reliability of cutaneous landmarks for the catheter length assessment during peripherally inserted central catheter insertion: A retrospective observational study." Journal of Vascular Access 21, no. 6 (March 31, 2020): 917–22. http://dx.doi.org/10.1177/1129729820911225.

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Introduction: Peripherally inserted central catheters are very common devices for short, medium and long-term therapies. Their performance is strictly dependent on the correct tip location, at the junction between the upper caval vein and the right atrium. It is very important to obtain an estimated measure of the catheter, in order to reach the cavo-atrial junction and optimize the catheter length. Estimated measures are often obtained using cutaneous landmarks. Objective: Evaluate the reliability of cutaneous landmark-based length estimation during catheter insertion. Identify any patient’s related factors that may affect cutaneous landmarks reliability. Methods: We used two distinct techniques and collected data about cutaneous landmark-based length estimation, electrocardiographic guided intravascular length, age, weight and height. We studied the reliability of possible correcting factors, balancing the error average by regression models, and we found and tested two different models of prediction. Results: A total number of 519 patients were studied. The average bias, between the two studied length assessment by cutaneous landmarks and electrocardiographic guided catheter length, were 3.77 ± 2.44 cm and 3.28 ± 2.57 cm, respectively. The analysed prediction models (deviance explained 43.5%, Akaike information criterion = 1313.67% and 43.4%, Akaike information criterion = 1313.92), fitted on the validation set, showed a root mean square error of 3.07 and 3.06. Conclusion: Landmark-based length estimation for preventive catheter length assessment seems to be unreliable, when associated with post-procedural tip location. They are useful for distal trimming catheters to optimize the ‘out of skin’ portion when associated with electrocardiographic tip location. Models identified for balancing bias are probably not useful.

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Yun, Hye Sun, Chang Min Hyun, Seong Hyeon Baek, Sang-Hwy Lee, and Jin Keun Seo. "A semi-supervised learning approach for automated 3D cephalometric landmark identification using computed tomography." PLOS ONE 17, no. 9 (September 28, 2022): e0275114. http://dx.doi.org/10.1371/journal.pone.0275114.

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Identification of 3D cephalometric landmarks that serve as proxy to the shape of human skull is the fundamental step in cephalometric analysis. Since manual landmarking from 3D computed tomography (CT) images is a cumbersome task even for the trained experts, automatic 3D landmark detection system is in a great need. Recently, automatic landmarking of 2D cephalograms using deep learning (DL) has achieved great success, but 3D landmarking for more than 80 landmarks has not yet reached a satisfactory level, because of the factors hindering machine learning such as the high dimensionality of the input data and limited amount of training data due to the ethical restrictions on the use of medical data. This paper presents a semi-supervised DL method for 3D landmarking that takes advantage of anonymized landmark dataset with paired CT data being removed. The proposed method first detects a small number of easy-to-find reference landmarks, then uses them to provide a rough estimation of the all landmarks by utilizing the low dimensional representation learned by variational autoencoder (VAE). The anonymized landmark dataset is used for training the VAE. Finally, coarse-to-fine detection is applied to the small bounding box provided by rough estimation, using separate strategies suitable for the mandible and the cranium. For mandibular landmarks, patch-based 3D CNN is applied to the segmented image of the mandible (separated from the maxilla), in order to capture 3D morphological features of mandible associated with the landmarks. We detect 6 landmarks around the condyle all at once rather than one by one, because they are closely related to each other. For cranial landmarks, we again use the VAE-based latent representation for more accurate annotation. In our experiment, the proposed method achieved a mean detection error of 2.88 mm for 90 landmarks using only 15 paired training data.

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Liu, Shuang, Hongli Xu, Yang Lin, and Lei Gao. "Visual Navigation for Recovering an AUV by Another AUV in Shallow Water." Sensors 19, no. 8 (April 20, 2019): 1889. http://dx.doi.org/10.3390/s19081889.

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Autonomous underwater vehicles (AUVs) play very important roles in underwater missions. However, the reliability of the automated recovery of AUVs has still not been well addressed. We propose a vision-based framework for automatically recovering an AUV by another AUV in shallow water. The proposed framework contains a detection phase for the robust detection of underwater landmarks mounted on the docking station in shallow water and a pose-estimation phase for estimating the pose between AUVs and underwater landmarks. We propose a Laplacian-of-Gaussian-based coarse-to-fine blockwise (LCB) method for the detection of underwater landmarks to overcome ambient light and nonuniform spreading, which are the two main problems in shallow water. We propose a novel method for pose estimation in practical cases where landmarks are broken or covered by biofouling. In the experiments, we show that our proposed LCB method outperforms the state-of-the-art method in terms of remote landmark detection. We then combine our proposed vision-based framework with acoustic sensors in field experiments to demonstrate its effectiveness in the automated recovery of AUVs.

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Lee, Yang Sun, Jang Woo Park, and Leonard Barolli. "A Localization Algorithm Based on AOA for Ad-Hoc Sensor Networks." Mobile Information Systems 8, no. 1 (2012): 61–72. http://dx.doi.org/10.1155/2012/986327.

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Knowledge of positions of sensor nodes in Wireless Sensor Networks (WSNs) will make possible many applications such as asset monitoring, object tracking and routing. In WSNs, the errors may happen in the measurement of distances and angles between pairs of nodes in WSN and these errors will be propagated to different nodes, the estimation of positions of sensor nodes can be difficult and have huge errors. In this paper, we will propose localization algorithm based on both distance and angle to landmark. So, we introduce a method of incident angle to landmark and the algorithm to exchange physical data such as distances and incident angles and update the position of a node by utilizing multiple landmarks and multiple paths to landmarks.

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Allen, Robert C., Daniel P. McDonald, and Michael J. Singer. "Landmark Direction and Distance Estimation in Large Scale Virtual Environments." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 41, no. 2 (October 1997): 1213–17. http://dx.doi.org/10.1177/1071181397041002109.

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The current paper describes our classification of errors participants made when estimating direction and distances in a large scale (2000 m × 2000 m) Virtual Environment (VE). Two VE configuration groups (Low or High Interactivity) traversed a 400 m route through one of two Virtual Terrain's (Distinctive or Non-Distinctive or Terrain 1 and 2, respectively) in 100 m increments. The High VE group used a treadmill to move through the VE with head tracked visual displays; the Low VE group used a joystick for movement and visual display control. Results indicate that as experience within either terrain increased, participants demonstrated an improved ability to directionally locate landmarks. Experience in the environment did not affect distance estimation accuracy. Terrain 1 participants were more accurate in locating proximal, as opposed to distal, landmarks. They also overestimated distances to near landmarks and underestimated distances to far landmarks. In Terrain 2, the Low VE group gave more accurate distance estimations. We believe this result can be explained in terms of increased task demands placed on the High VE Group.

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Keelson, Benyameen, Luca Buzzatti, Jakub Ceranka, Adrián Gutiérrez, Simone Battista, Thierry Scheerlinck, Gert Van Gompel, et al. "Automated Motion Analysis of Bony Joint Structures from Dynamic Computer Tomography Images: A Multi-Atlas Approach." Diagnostics 11, no. 11 (November 7, 2021): 2062. http://dx.doi.org/10.3390/diagnostics11112062.

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Dynamic computer tomography (CT) is an emerging modality to analyze in-vivo joint kinematics at the bone level, but it requires manual bone segmentation and, in some instances, landmark identification. The objective of this study is to present an automated workflow for the assessment of three-dimensional in vivo joint kinematics from dynamic musculoskeletal CT images. The proposed method relies on a multi-atlas, multi-label segmentation and landmark propagation framework to extract bony structures and detect anatomical landmarks on the CT dataset. The segmented structures serve as regions of interest for the subsequent motion estimation across the dynamic sequence. The landmarks are propagated across the dynamic sequence for the construction of bone embedded reference frames from which kinematic parameters are estimated. We applied our workflow on dynamic CT images obtained from 15 healthy subjects on two different joints: thumb base (n = 5) and knee (n = 10). The proposed method resulted in segmentation accuracies of 0.90 ± 0.01 for the thumb dataset and 0.94 ± 0.02 for the knee as measured by the Dice score coefficient. In terms of motion estimation, mean differences in cardan angles between the automated algorithm and manual segmentation, and landmark identification performed by an expert were below 1°. Intraclass correlation (ICC) between cardan angles from the algorithm and results from expert manual landmarks ranged from 0.72 to 0.99 for all joints across all axes. The proposed automated method resulted in reproducible and reliable measurements, enabling the assessment of joint kinematics using 4DCT in clinical routine.

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Micheler, Carina M., Jan J. Lang, Nikolas J. Wilhelm, Igor Lazic, Florian Hinterwimmer, Christian Fritz, Rüdiger von Eisenhart-Rothe, Michael F. Zäh, and Rainer H. H. Burgkart. "Scaling Methods of the Pelvis without Distortion for the Analysis of Bone Defects." Current Directions in Biomedical Engineering 8, no. 2 (August 1, 2022): 797–800. http://dx.doi.org/10.1515/cdbme-2022-1203.

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Abstract For the development of new types of hip implants for acetabulum revision, it is beneficial to analyse the acetabular defects of the indication group in advance. In order to be able to specially compare the bone defects with each other, a normalisation and accompanying scaling of the pelvis is necessary. Uniform scaling is required so that the bone structures are not distorted. In the following study, three scaling methods based on the minimal bounding box and sphere principle are compared with a method using 14 landmarks on the pelvis.The landmark method is applied to determine the true scaling factor. For the comparison of the different methods, 40 female pelvic models with an acetabular defect are analysed. In the comparison of the scaling methods, the method using minimal bounding spheres shows the least deviation from the landmark method (mean difference 3.30 ± 2.17 %). Due to the fact that no preprocessing (definition of the landmarks) is required and the fast implementation of the algorithm, the minimal bounding sphere is to be preferred to the landmark method for a fast size estimation.

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Gdoura, Ahmed, Markus Degünther, Birgit Lorenz, and Alexander Effland. "Combining CNNs and Markov-like Models for Facial Landmark Detection with Spatial Consistency Estimates." Journal of Imaging 9, no. 5 (May 22, 2023): 104. http://dx.doi.org/10.3390/jimaging9050104.

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The accurate localization of facial landmarks is essential for several tasks, including face recognition, head pose estimation, facial region extraction, and emotion detection. Although the number of required landmarks is task-specific, models are typically trained on all available landmarks in the datasets, limiting efficiency. Furthermore, model performance is strongly influenced by scale-dependent local appearance information around landmarks and the global shape information generated by them. To account for this, we propose a lightweight hybrid model for facial landmark detection designed specifically for pupil region extraction. Our design combines a convolutional neural network (CNN) with a Markov random field (MRF)-like process trained on only 17 carefully selected landmarks. The advantage of our model is the ability to run different image scales on the same convolutional layers, resulting in a significant reduction in model size. In addition, we employ an approximation of the MRF that is run on a subset of landmarks to validate the spatial consistency of the generated shape. This validation process is performed against a learned conditional distribution, expressing the location of one landmark relative to its neighbor. Experimental results on popular facial landmark localization datasets such as 300 w, WFLW, and HELEN demonstrate the accuracy of our proposed model. Furthermore, our model achieves state-of-the-art performance on a well-defined robustness metric. In conclusion, the results demonstrate the ability of our lightweight model to filter out spatially inconsistent predictions, even with significantly fewer training landmarks.

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Ye, Jianghao, Ying Cui, Xiang Pan, Herong Zheng, Dongyan Guo, and Yanan Ren. "An improved boundary-aware face alignment using stacked dense U-Nets." International Journal of Advanced Robotic Systems 17, no. 4 (July 1, 2020): 172988142094090. http://dx.doi.org/10.1177/1729881420940900.

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Facial landmark localization is still a challenge task in the unconstrained environment with influences of significant variation conditions such as facial pose, shape, expression, illumination, and occlusions. In this work, we present an improved boundary-aware face alignment method by using stacked dense U-Nets. The proposed method consists of two stages: a boundary heatmap estimation stage to learn the facial boundary lines and a facial landmark localization stage to predict the final face alignment result. With the constraint of boundary lines, facial landmarks are unified as a whole facial shape. Hence, the unseen landmarks in a shape with occlusions can be better estimated by message passing with other landmarks. By introducing the stacked dense U-Nets for feature extraction, the capacity of the model is improved. Experiments and comparisons on public datasets show that the proposed method obtains better performance than the baselines, especially for facial images with large pose variation, shape variation, and occlusions.

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Maltzahn, Niklas, Rune Hoff, Odd O. Aalen, Ingrid S. Mehlum, Hein Putter, and Jon Michael Gran. "A hybrid landmark Aalen-Johansen estimator for transition probabilities in partially non-Markov multi-state models." Lifetime Data Analysis 27, no. 4 (September 30, 2021): 737–60. http://dx.doi.org/10.1007/s10985-021-09534-4.

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AbstractMulti-state models are increasingly being used to model complex epidemiological and clinical outcomes over time. It is common to assume that the models are Markov, but the assumption can often be unrealistic. The Markov assumption is seldomly checked and violations can lead to biased estimation of many parameters of interest. This is a well known problem for the standard Aalen-Johansen estimator of transition probabilities and several alternative estimators, not relying on the Markov assumption, have been suggested. A particularly simple approach known as landmarking have resulted in the Landmark-Aalen-Johansen estimator. Since landmarking is a stratification method a disadvantage of landmarking is data reduction, leading to a loss of power. This is problematic for “less traveled” transitions, and undesirable when such transitions indeed exhibit Markov behaviour. Introducing the concept of partially non-Markov multi-state models, we suggest a hybrid landmark Aalen-Johansen estimator for transition probabilities. We also show how non-Markov transitions can be identified using a testing procedure. The proposed estimator is a compromise between regular Aalen-Johansen and landmark estimation, using transition specific landmarking, and can drastically improve statistical power. We show that the proposed estimator is consistent, but that the traditional variance estimator can underestimate the variance of both the hybrid and landmark estimator. Bootstrapping is therefore recommended. The methods are compared in a simulation study and in a real data application using registry data to model individual transitions for a birth cohort of 184 951 Norwegian men between states of sick leave, disability, education, work and unemployment.

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Rowell, N., M. N. Dunstan, S. M. Parkes, J. Gil-Fernández, I. Huertas, and S. Salehi. "Autonomous visual recognition of known surface landmarks for optical navigation around asteroids." Aeronautical Journal 119, no. 1220 (October 2015): 1193–222. http://dx.doi.org/10.1017/s0001924000011210.

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AbstractWe present an autonomous visual landmark recognition and pose estimation algorithm designed for use in navigation of spacecraft around small asteroids. Landmarks are selected as generic points on the asteroid surface that produce strong Harris corners in an image under a wide range in viewing and illumination conditions; no particular type of morphological feature is required. The set of landmarks is triangulated to obtain a tightly fitting mesh representing an optimal low resolution model of the natural asteroid shape, which is used onboard to determine the visibility of each landmark and enables the algorithm to work with highly concave bodies. The shape model is also used to estimate the centre of brightness of the asteroid and eliminate large translation errors prior to the main landmark recognition stage. The algorithm works by refining an initial estimate of the spacecraft position and orientation. Tests with real and synthetic images show good performance under realistic noise conditions. Using simulated images, the median landmark recognition error is 2m, and the error on the spacecraft position in the asteroid body frame is reduced from 45m to 21m at a range of 2km from the surface. With real images the translation error at 8km to the surface increases from 107m to 119m, due mainly to the larger range and lack of sensitivity to translations along the camera boresight. The median number of landmarks detected in the simulated and real images is 59 and 44 respectively. This algorithm was partly developed and tested during industrial studies for the European Space Agency’s Marco Polo-R asteroid sample return mission.

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Wen, Zhihua, and Michael Rabinovich. "Network distance estimation with dynamic landmark triangles." ACM SIGMETRICS Performance Evaluation Review 36, no. 1 (June 12, 2008): 433–34. http://dx.doi.org/10.1145/1384529.1375507.

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Lee, Jyun-Cheng, Chih-Chun Chen, Chang-Te Shen, and Ying-Chih Lai. "Landmark-Based Scale Estimation and Correction of Visual Inertial Odometry for VTOL UAVs in a GPS-Denied Environment." Sensors 22, no. 24 (December 9, 2022): 9654. http://dx.doi.org/10.3390/s22249654.

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With the rapid development of technology, unmanned aerial vehicles (UAVs) have become more popular and are applied in many areas. However, there are some environments where the Global Positioning System (GPS) is unavailable or has the problem of GPS signal outages, such as indoor and bridge inspections. Visual inertial odometry (VIO) is a popular research solution for non-GPS navigation. However, VIO has problems of scale errors and long-term drift. This study proposes a method to correct the position errors of VIO without the help of GPS information for vertical takeoff and landing (VTOL) UAVs. In the initial process, artificial landmarks are utilized to improve the positioning results of VIO by the known landmark information. The position of the UAV is estimated by VIO. Then, the accurate position is estimated by the extended Kalman filter (EKF) with the known landmark, which is used to obtain the scale correction using the least squares method. The Inertial Measurement Unit (IMU) data are used for integration in the time-update process. The EKF can be updated with two measurements. One is the visual odometry (VO) estimated directly by a landmark. The other is the VIO with scale correction. When the landmark is detected during takeoff phase, or the UAV is returning to the takeoff location during landing phase, the trajectory estimated by the landmark is used to update the scale correction. At the beginning of the experiments, preliminary verification was conducted on the ground. A self-developed UAV equipped with a visual–inertial sensor to collect data and a high-precision real time kinematic (RTK) to verify trajectory are applied to flight tests. The experimental results show that the method proposed in this research effectively solves the problems of scale and the long-term drift of VIO.

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Liu, Pin-Ling, Chien-Chi Chang, Jia-Hua Lin, and Yoshiyuki Kobayashi. "Simple benchmarking method for determining the accuracy of depth cameras in body landmark location estimation: Static upright posture as a measurement example." PLOS ONE 16, no. 7 (July 21, 2021): e0254814. http://dx.doi.org/10.1371/journal.pone.0254814.

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To evaluate the postures in ergonomics applications, studies have proposed the use of low-cost, marker-less, and portable depth camera-based motion tracking systems (DCMTSs) as a potential alternative to conventional marker-based motion tracking systems (MMTSs). However, a simple but systematic method for examining the estimation errors of various DCMTSs is lacking. This paper proposes a benchmarking method for assessing the estimation accuracy of depth cameras for full-body landmark location estimation. A novel alignment board was fabricated to align the coordinate systems of the DCMTSs and MMTSs. The data from an MMTS were used as a reference to quantify the error of using a DCMTS to identify target locations in a 3-D space. To demonstrate the proposed method, the full-body landmark location tracking errors were evaluated for a static upright posture using two different DCMTSs. For each landmark, we compared each DCMTS (Kinect system and RealSense system) with an MMTS by calculating the Euclidean distances between symmetrical landmarks. The evaluation trials were performed twice. The agreement between the tracking errors of the two evaluation trials was assessed using intraclass correlation coefficient (ICC). The results indicate that the proposed method can effectively assess the tracking performance of DCMTSs. The average errors (standard deviation) for the Kinect system and RealSense system were 2.80 (1.03) cm and 5.14 (1.49) cm, respectively. The highest average error values were observed in the depth orientation for both DCMTSs. The proposed method achieved high reliability with ICCs of 0.97 and 0.92 for the Kinect system and RealSense system, respectively.

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Karkasina, Dafni, Margarita Kokla, and Eleni Tomai. "Investigating drivers’ geospatial abilities in unfamiliar environments." AGILE: GIScience Series 2 (June 4, 2021): 1–10. http://dx.doi.org/10.5194/agile-giss-2-3-2021.

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Abstract. The prominence of landmarks in aiding pedestrian navigation has been highlighted in various studies; people rely strongly on visual landmarks, especially when navigating in unfamiliar environments. The paper describes the design and implementation of a study for assessing drivers’ spatial abilities, when navigating in an unfamiliar environment. Two types of route directions based on references to either landmarks or street names were given to two groups of participants. Three geospatial learning tasks are used to evaluate these abilities: map sketching, distance, and direction estimation. The findings showed that landmark-based route instructions help drivers develop a better cognitive map of the route. On the other hand, instructions either based on landmarks or on street information do not have an effect on distance or direction estimates. Nonetheless, qualitative analysis of directions and distances estimations gave interesting results. Findings associated with self-assessment of environmental spatial abilities using the Santa Barbara Sense of Direction Scale (SBSOD) seem to support prediction of at least one of the drivers’ abilities among those assessed in this study.

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Koh, Kyoung C., Jae S. Kim, and Hyung S. Cho. "A position estimation system for mobile robots using a monocular image of a 3-D landmark." Robotica 12, no. 5 (September 1994): 431–41. http://dx.doi.org/10.1017/s0263574700017987.

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SUMMARYThis paper presents an absolute position estimation system for a mobile robot moving on a flat surface. In this system, a 3-D landmark with four coplanar points and a non-coplanar point is utilized to improve the accuracy of position estimation and to guide the robot during navigation. Applying theoretical analysis, we investigate the image sensitivity of the proposed 3-D landmark compared with the conventional 2-D landmark. In the camera calibration stage of the experiments, we employ a neural network as a computational tool. The neural network is trained from a set of learning data collected at various points around the mark so that the extrinsic and intrinsic parameters of the camera system can be resolved. The overall estimation algorithm from the mark identification to the position determination is implemented in a 32-bit personal computer with an image digitizer and an arithmetic accelerator. To demonstrate the effectiveness of the proposed 3-D landmark and the neural network-based calibration scheme, a series of navigation experiments were performed on a wheeled mobile robot (LCAR) in an indoor environment. The results show the feasibility of the position estimation system applicable to mobile robot's real-time navigation.

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Senejohnny, Danial, and Mehrzad Namvar. "A predictor-based attitude and position estimation for rigid bodies moving in planar space by using delayed landmark measurements." Robotica 35, no. 6 (April 18, 2016): 1415–30. http://dx.doi.org/10.1017/s0263574716000187.

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SUMMARYThis paper proposes a globally and exponentially convergent predictive observer for attitude and position estimation based on landmark measurements and velocity (angular and linear) readings. It is assumed that landmark measurements are available with time-delay. The maximum value of the sensor delay under which the estimation error converges to zero is calculated. Synthesis of the observer is based on a representation of rigid-body kinematics and sensor delay, formulated via ordinary and partial differential equations (ODE-PDE). Observability condition specifies necessary and sufficient landmark configuration for convergence of attitude and position estimation error to zero. Finally, for implementation purposes, a PDE-free realization of the predictive observer is proposed. Simulation results are presented to demonstrate performance and convergence properties of the predictive observer in case of a wheeled mobile robot.

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Yoshinaga, Tsukasa, Kazunori Nozaki, Osamu Kondo, and Akiyoshi Iida. "Estimation of sibilant groove formation and sound generation from early hominin jawbones." JASA Express Letters 2, no. 4 (April 2022): 045203. http://dx.doi.org/10.1121/10.0010209.

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The speech production capability of sibilant fricatives of early hominin was assessed by interpolating the modern human vocal tract to an Australopithecine specimen based on the jawbone landmarks, and then simulating the airflow and sound generation. The landmark interpolation demonstrates the possibility to form the sibilant groove in the anterior part of the oral tract, and results of the aeroacoustic simulation indicate that the early hominins had the potential to produce the fricative broadband noise with a constant supply of airflow to the oral cavity, although the ancestor's tongue deformation ability is still uncertain, and the results are highly speculative.

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Liu, Xin Ying, and Ping Ping Liu. "LSD Based Pose and Position Estimation for UAV Landing." Applied Mechanics and Materials 631-632 (September 2014): 602–5. http://dx.doi.org/10.4028/www.scientific.net/amm.631-632.602.

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Currently, there has been growing interest in unmanned aerial vehicle (UAV) during the landing. With the widespread use of the UAVs, a more precise estimation on pose and position in the process of landing is required to support the higher-level applications. In this paper, the estimation of pose and position based on the line segment detection (LSD) is proposed. By applying a vision camera, a landmark is detected using the effective LSD algorithm. Then a line-based vision model is built to calculate the pose and position of the UAV. Experimental results show that the state solutions of the proposed method are effective with different shape of landmarks, and the accuracy is minute-level in pose angle error and centimeter-level in position error.

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Sayadi, Mehrab, Najaf Zare, Armin Attar, and Seyyed Mohammad Taghi Ayatollahi. "Improved Landmark Dynamic Prediction Model to Assess Cardiovascular Disease Risk in On-Treatment Blood Pressure Patients: A Simulation Study and Post Hoc Analysis on SPRINT Data." BioMed Research International 2020 (April 23, 2020): 1–16. http://dx.doi.org/10.1155/2020/2905167.

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Landmark model (LM) is a dynamic prediction model that uses a longitudinal biomarker in time-to-event data to make prognosis prediction. This study was designed to improve this model and to apply it to assess the cardiovascular risk in on-treatment blood pressure patients. A frailty parameter was used in LM, landmark frailty model (LFM), to account the frailty of the patients and measure the correlation between different landmarks. The proposed model was compared with LM in different scenarios respecting data missing status, sample size (100, 200, and 400), landmarks (6, 12, 24, and 48), and failure percentage (30, 50, and 100%). Bias of parameter estimation and mean square error as well as deviance statistic between models were compared. Additionally, discrimination and calibration capability as the goodness of fit of the model were evaluated using dynamic concordance index (DCI), dynamic prediction error (DPE), and dynamic relative prediction error (DRPE). The proposed model was performed on blood pressure data, obtained from systolic blood pressure intervention trial (SPRINT), in order to calculate the cardiovascular risk. Dynpred, coxme, and coxphw packages in the R.3.4.3 software were used. It was proved that our proposed model, LFM, had a better performance than LM. Parameter estimation in LFM was closer to true values in comparison to that in LM. Deviance statistic showed that there was a statistically significant difference between the two models. In the landmark numbers 6, 12, and 24, the LFM had a higher DCI over time and the three landmarks showed better performance in discrimination. Both DPE and DRPE in LFM were lower in comparison to those in LM over time. It was indicated that LFM had better calibration in comparison to its peer. Moreover, real data showed that the structure of prognostic process was predicted better in LFM than in LM. Accordingly, it is recommended to use the LFM model for assessing cardiovascular risk due to its better performance.

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Korolov, Volodimir, Stepan Savchuk, Olha Korolova, Ihor Milkovich, and Yaroslav Zaec. "Mathematical Model for Errors Estimation of Object’s Location Parameters Determination Using Flying Platform." Baltic Surveying 10 (June 1, 2019): 26–30. http://dx.doi.org/10.22616/j.balticsurveying.2019.003.

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Some tasks require identification of landmarks in districts beyond the reach of existing optical observation facilities. The accuracy of their determination significantly affects the effectiveness of the use necessary equipment during the task. In the paper an algorithm to determine the object parameters using a flying platform is offered. An observation point is installed which is equipped with a navigation system to solve this task. This ensures its orientation and positioning. From the observation point an aerial observation point is displayed. It is suggested to use a flying platform. The coordinates of the flying platform are determined relative to the observation point, the coordinates of the landmarks are determined relative to the flying platform. The mathematical model of the estimation error determination of object coordinates with the help of a flying platform is proposed. The analysis of errors in determining the parameters of the object using a flying platform is conducted. Analysis of the results of mathematical modeling is conducted using the package of applications Mathcad. The dependence of these parameters on the relative position of the observation point, platforms and object is examined. It is shown that the main contribution to the error of determining the coordinates of the landmark is given by the errors of determining the observation point location and measuring the range. An analytical correlation is obtained, which allows to estimate the errors of determining the coordinates of the landmark using the flying platform with known errors in determining the observation point coordinates and the range determination of the range finder.

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Putter, Hein, and Cristian Spitoni. "Non-parametric estimation of transition probabilities in non-Markov multi-state models: The landmark Aalen–Johansen estimator." Statistical Methods in Medical Research 27, no. 7 (October 20, 2016): 2081–92. http://dx.doi.org/10.1177/0962280216674497.

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The topic non-parametric estimation of transition probabilities in non-Markov multi-state models has seen a remarkable surge of activity recently. Two recent papers have used the idea of subsampling in this context. The first paper, by de Uña Álvarez and Meira-Machado, uses a procedure based on (differences between) Kaplan–Meier estimators derived from a subset of the data consisting of all subjects observed to be in the given state at the given time. The second, by Titman, derived estimators of transition probabilities that are consistent in general non-Markov multi-state models. Here, we show that the same idea of subsampling, used in both these papers, combined with the Aalen–Johansen estimate of the state occupation probabilities derived from that subset, can also be used to obtain a relatively simple and intuitive procedure which we term landmark Aalen–Johansen. We show that the landmark Aalen–Johansen estimator yields a consistent estimator of the transition probabilities in general non-Markov multi-state models under the same conditions as needed for consistency of the Aalen–Johansen estimator of the state occupation probabilities. Simulation studies show that the landmark Aalen–Johansen estimator has good small sample properties and is slightly more efficient than the other estimators.

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Song, Meijia, Hong Liu, Wei Shi, and Xia Li. "PCLoss: Fashion Landmark Estimation with Position Constraint Loss." Pattern Recognition 118 (October 2021): 108028. http://dx.doi.org/10.1016/j.patcog.2021.108028.

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Xie, Zhifeng, Zhipeng Zhou, Zhaosheng Wang, Huiming Ding, and Lizhuang Ma. "Multi-Scale Spatial Feature-Guided Cloth Landmark Estimation." Journal of Computer-Aided Design & Computer Graphics 34, no. 11 (November 1, 2022): 1763–71. http://dx.doi.org/10.3724/sp.j.1089.2022.19189.

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Chen, Yuzhong, Yang Yu, and Guolong Chen. "Shortest distance estimation in large scale graphs." Engineering Computations 31, no. 8 (October 28, 2014): 1635–47. http://dx.doi.org/10.1108/ec-11-2012-0286.

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Purpose – Shortest distance query between a pair of nodes in a graph is a classical problem with a wide variety of applications. Exact methods for this problem are infeasible for large-scale graphs such as social networks with hundreds of millions of users and links due to their high complexity of time and space. The purpose of this paper is to propose a novel landmark selection strategy which can estimate the shortest distances in large-scale graphs and clarify the efficiency and accuracy of the proposed strategy in comparison with currently used strategies. Design/methodology/approach – Different from existing strategies, the landmark selection problem is regarded as a binary combinational optimization problem consisting of two optimization objectives and one constraint. Further, the original binary combinational optimization problem with constraints is transformed to a proper form of optimization objectives without any additional constraints and the equivalence of solutions is proved. Finally the solution of the optimization problem is performed with a modified multi-objective particle swarm optimization (MOPSO) integrating the mutation operator and crossover operator of genetic algorithm. Findings – Four real networks of large scale are used as data sets to carry out the experiments and the experiment results show that the proposed strategy improves both of the accuracy and time efficiency to perform shortest distance estimation in large scale graph compared to other currently used strategies. Originality/value – This paper proposes a novel landmark selection strategy which regards the landmark selection problem as a binary combinational optimization problem. The original binary combinational optimization problem with constraints is transformed to a proper form of optimization objectives without constraints and the equivalence of these two optimization problems is proved. This novel strategy also utilizes a modified MOPSO integrating the mutation operator and crossover operator of genetic algorithm.

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Oh, Chahyun, Boohwi Hong, Yumin Jo, Woosuk Chung, Hoseop Kim, Suyeon Shin, Ah Young Choi, et al. "A retrospective comparison for prediction of optimal length of right subclavian vein catheterization in infants: landmark-based estimation vs. linear regression model." Anesthesia and Pain Medicine 16, no. 3 (July 31, 2021): 258–65. http://dx.doi.org/10.17085/apm.21021.

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Background: The optimal insertion length for right subclavian vein catheterization in infants has not been determined. This study retrospectively compared landmark-based and linear regression model-based estimation of optimal insertion length for right subclavian vein catheterization in pediatric patients of corrected age < 1 year. Methods: Fifty catheterizations of the right subclavian vein were analyzed. The landmark related distances were: from the needle insertion point (I) to the tip of the sternal head of the right clavicle (A) and from A to the midpoint (B) of the perpendicular line drawn from the sternal head of the right clavicle to the line connecting the nipples. The optimal length of insertion was retrospectively determined by reviewing post-procedural chest radiographs. Estimates using a landmark-based equation (IA + AB – intercept) and a linear regression model were compared with the optimal length of insertion. Results: A landmark-based equation was determined as IA + AB – 5. The mean difference between the landmark-based estimate and the optimal insertion length was 1.0 mm (95% limits of agreement –18.2 to 20.3 mm). The mean difference between the linear regression model (26.681 – 4.014 × weight + 0.576 × IA + 0.537 × AB – 0.482 × postmenstrual age) and the optimal insertion length was 0 mm (95% limits of agreement –16.7 to 16.7 mm). The difference between the estimates using these two methods was not significant. Conclusion: A simple landmark-based equation may be useful for estimating optimal insertion length in pediatric patients of corrected age < 1 year undergoing right subclavian vein catheterization.

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Rauniyar, Shyam, Sameer Bhalla, Daegyun Choi, and Donghoon Kim. "EKF-SLAM for Quadcopter Using Differential Flatness-Based LQR Control." Electronics 12, no. 5 (February 24, 2023): 1113. http://dx.doi.org/10.3390/electronics12051113.

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SLAM (Simultaneous Localization And Mapping) in unmanned aerial vehicles can be an advantageous proposition during dangerous missions where aggressive maneuvers are paramount. This paper proposes to achieve it for a quadcopter using a differential flatness-based linear quadratic regulator while utilizing sensor measurements of an inertial measurement unit and light detection and ranging considering sensors’ constraints, such as a limited sensing range and field of view. Additionally, a strategy to reduce the computational effort of Extended Kalman Filter-based SLAM (EKF-SLAM) is proposed. To validate the performance of the proposed approach, this work considers a quadcopter traversing an 8-shape trajectory for two scenarios of known and unknown landmarks. The estimation errors for the quadcopter states are comparable for both cases. The accuracy of the proposed method is evident from the Root-Mean-Square Errors (RMSE) of 0.04 m, 0.04 m/s, and 0.34 deg for the position, velocity, and attitude estimation of the quadcopter, respectively, including the RMSE of 0.03 m for the landmark position estimation. Lastly, the averaged computational time for each step of EKF-SLAM with respect to the number of landmarks can help to strategically choose the respective number of landmarks for each step to maximize the use of sensor data and improve performance.

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Tam, Andy Yiu-Chau, Li-Wen Zha, Bryan Pak-Hei So, Derek Ka-Hei Lai, Ye-Jiao Mao, Hyo-Jung Lim, Duo Wai-Chi Wong, and James Chung-Wai Cheung. "Depth-Camera-Based Under-Blanket Sleep Posture Classification Using Anatomical Landmark-Guided Deep Learning Model." International Journal of Environmental Research and Public Health 19, no. 20 (October 18, 2022): 13491. http://dx.doi.org/10.3390/ijerph192013491.

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Emerging sleep health technologies will have an impact on monitoring patients with sleep disorders. This study proposes a new deep learning model architecture that improves the under-blanket sleep posture classification accuracy by leveraging the anatomical landmark feature through an attention strategy. The system used an integrated visible light and depth camera. Deep learning models (ResNet-34, EfficientNet B4, and ECA-Net50) were trained using depth images. We compared the models with and without an anatomical landmark coordinate input generated with an open-source pose estimation model using visible image data. We recruited 120 participants to perform seven major sleep postures, namely, the supine posture, prone postures with the head turned left and right, left- and right-sided log postures, and left- and right-sided fetal postures under four blanket conditions, including no blanket, thin, medium, and thick. A data augmentation technique was applied to the blanket conditions. The data were sliced at an 8:2 training-to-testing ratio. The results showed that ECA-Net50 produced the best classification results. Incorporating the anatomical landmark features increased the F1 score of ECA-Net50 from 87.4% to 92.2%. Our findings also suggested that the classification performances of deep learning models guided with features of anatomical landmarks were less affected by the interference of blanket conditions.

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Neath, Andrew A., and Natalie Langenfeld. "A Note on the Comparison of the Bayesian and Frequentist Approaches to Estimation." Advances in Decision Sciences 2012 (October 22, 2012): 1–12. http://dx.doi.org/10.1155/2012/764254.

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Samaniego and Reneau presented a landmark study on the comparison of Bayesian and frequentist point estimators. Their findings indicate that Bayesian point estimators work well in more situations than were previously suspected. In particular, their comparison reveals how a Bayesian point estimator can improve upon a frequentist point estimator even in situations where sharp prior knowledge is not necessarily available. In the current paper, we show that similar results hold when comparing Bayesian and frequentist interval estimators. Furthermore, the development of an appropriate interval estimator comparison offers some further insight into the estimation problem.

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Dogancay, Kutluyil. "Self-localization from landmark bearings using pseudolinear estimation techniques." IEEE Transactions on Aerospace and Electronic Systems 50, no. 3 (July 2014): 2361–68. http://dx.doi.org/10.1109/taes.2014.130461.

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Cappello, Angelo, Aurelio Cappozzo, Pier Francesco La Palombara, Luigi Lucchetti, and Alberto Leardini. "Multiple anatomical landmark calibration for optimal bone pose estimation." Human Movement Science 16, no. 2-3 (April 1997): 259–74. http://dx.doi.org/10.1016/s0167-9457(96)00055-3.

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Stoyan, D. "Estimation of Distances and Variances in Bookstein's Landmark Model." Biometrical Journal 32, no. 7 (January 19, 2007): 843–49. http://dx.doi.org/10.1002/bimj.4710320712.

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Tully, Stephen, George Kantor, and Howie Choset. "A Single-Step Maximum A Posteriori Update for Bearing-Only SLAM." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (July 4, 2010): 1252–57. http://dx.doi.org/10.1609/aaai.v24i1.7736.

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This paper presents a novel recursive maximum a posteriori update for the Kalman formulation of undelayed bearing-only SLAM. The estimation update step is cast as an optimization problem for which we can prove the global minimum is reachable via a bidirectional search using Gauss-Newton's method along a one-dimensional manifold. While the filter is designed for mapping just one landmark, it is easily extended to full-scale multiple-landmark SLAM. We provide this extension via a formulation of bearing-only FastSLAM. With experiments, we demonstrate accurate and convergent estimation in situations where an EKF solution would diverge.

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Bloesch, Michael, Michael Burri, Sammy Omari, Marco Hutter, and Roland Siegwart. "Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback." International Journal of Robotics Research 36, no. 10 (September 2017): 1053–72. http://dx.doi.org/10.1177/0278364917728574.

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This paper presents a visual-inertial odometry framework that tightly fuses inertial measurements with visual data from one or more cameras, by means of an iterated extended Kalman filter. By employing image patches as landmark descriptors, a photometric error is derived, which is directly integrated as an innovation term in the filter update step. Consequently, the data association is an inherent part of the estimation process and no additional feature extraction or matching processes are required. Furthermore, it enables the tracking of noncorner-shaped features, such as lines, and thereby increases the set of possible landmarks. The filter state is formulated in a fully robocentric fashion, which reduces errors related to nonlinearities. This also includes partitioning of a landmark’s location estimate into a bearing vector and distance and thereby allows an undelayed initialization of landmarks. Overall, this results in a compact approach, which exhibits a high level of robustness with respect to low scene texture and motion blur. Furthermore, there is no time-consuming initialization procedure and pose estimates are available starting at the second image frame. We test the filter on different real datasets and compare it with other state-of-the-art visual-inertial frameworks. Experimental results show that robust localization with high accuracy can be achieved with this filter-based framework.

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Yekkehfallah, Majid, Ming Yang, Zhiao Cai, Liang Li, and Chuanxiang Wang. "Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots." Robotica 39, no. 10 (February 22, 2021): 1816–33. http://dx.doi.org/10.1017/s0263574720001526.

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SUMMARYLocalization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

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Tsai, Fuan, Yih Shyh Chiou, and Huan Chang. "A Positioning Scheme Combining Kalman Filtering with Vision Assisting for Wireless Sensor Networks." Applied Mechanics and Materials 284-287 (January 2013): 2009–14. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.2009.

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This paper presents the performance of an adaptive location estimator combining Kalman filtering (KF) scheme with vision-assisted scheme for wireless sensor networks. To improve the location accuracy, a KF tracking scheme is employed at a mobile terminal to track variations of the location estimate. In addition, with a vision-assisted calibration technique based on the normalized cross-correlation scheme, the proposed approach is an accuracy enhancement procedure that effectively removes system errors causing uncertainty in measuring a dynamic environment. Therefore, using the vision-assisted approach to estimate the locations of the reference nodes as landmarks, a KF-based scheme with the landmark information can calibrate the location estimation and improve the corner effect. The experimental results demonstrate that more than 60 percent of the location estimates computed from the proposed approach have error distances less than 1.4 meters in a ZigBee positioning platform. As compared with the non-tracking algorithm and non-vision-assisted approach, the proposed algorithm can achieve reasonably good performance.

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Ajanović, Zurifa, Uzeir Ajanović, Lejla Dervišević, Haris Hot, Alma Voljevica, Elvira Talović, Emina Dervišević, Selma Hašimbegović, and Aida Sarač-Hadžihalilović. "A Geometric Morphometrics Approach for Sex Estimation Based on the Orbital Region of Human Skulls from Bosnian Population." Scanning 2023 (April 14, 2023): 1–9. http://dx.doi.org/10.1155/2023/2223138.

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Background. Understanding the anatomy and morphological variability of the orbital region is of great importance in clinical practice, forensic medicine, and biological anthropology. Several methods are used to estimate sex based on the skeleton or parts of the skeleton: classic methods and the geometric morphometric method. The objective of this research was to analyse sex estimation of the orbital region on a sample of skulls from a Bosnian population using the geometric morphometric method. Materials and Methods. The research was conducted on three-dimensional models of 211 human adult skulls (139 males and 72 females) from the Osteological Collection at the Faculty of Medicine in Sarajevo. The skulls were recorded using a laser scanner to obtain skull 3D models. We marked 12 landmarks on each model to analyse sexual dimorphism. Landmarks were marked using the program Landmark Editor. After marking the landmarks, we used the MorphoJ program to analyse the morphological variability between male and female orbital regions. Results. After Procrustes superimposition, generating a covariant matrix, and introducing sex as a variable for classification, a discriminant functional analysis (DFA) was applied which determined the estimation for males with 86.33% accuracy and for females with 88.89% based on the form of the orbital region. The results of regression analysis showed that the size of the orbital region has a statistically significant effect on its shape’s sexual dimorphism. After excluding the influence of size and providing DFA, we concluded that sex estimation was possible with 82.01% accuracy for males and 80.55% accuracy for females based on the shape of the orbital region in the examined sample. Conclusion. Sex estimation based on the orbital region was possible with more than 80% accuracy for both sexes, which is a high percentage of correct estimation. Therefore, we recommend using the orbital region of the skull for sex estimation.

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Jiang, Suhui, Yu Kong, and Yun Fu. "Deep Geo-Constrained Auto-Encoder for Non-Landmark GPS Estimation." IEEE Transactions on Big Data 5, no. 2 (June 1, 2019): 120–33. http://dx.doi.org/10.1109/tbdata.2017.2773096.

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Moeini, Amir, and Mehrzad Namvar. "Global attitude/position estimation using landmark and biased velocity measurements." IEEE Transactions on Aerospace and Electronic Systems 52, no. 2 (April 2016): 852–62. http://dx.doi.org/10.1109/taes.2015.140626.

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Chang, Feng-Ju, Anh Tuan Tran, Tal Hassner, Iacopo Masi, Ram Nevatia, and Gérard Medioni. "Deep, Landmark-Free FAME: Face Alignment, Modeling, and Expression Estimation." International Journal of Computer Vision 127, no. 6-7 (February 13, 2019): 930–56. http://dx.doi.org/10.1007/s11263-019-01151-x.

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Journal articles on the topic 'Landmark Estimation'

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