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Journal articles on the topic 'Indoor robotics'

Author: Grafiati
Published: 22 March 2025

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1

Cooper, Martin. "Paw-Sitive Reception for Robot Guide Dog." ITNOW 66, no. 2 (May 20, 2024): 30–31. http://dx.doi.org/10.1093/itnow/bwae048a.

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Abstract Working in partnership with charities, AI and robotics experts from the University of Glasgow have demonstrated a robotic guide dog designed to help partially sighted people navigate indoor spaces.
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Cooper, Martin. "Paw-Sitive Reception for Robot Guide Dog." ITNOW 66, no. 2 (May 1, 2024): 30–31. http://dx.doi.org/10.1093/itnow/bwae048.

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Abstract Working in partnership with charities, AI and robotics experts from the University of Glasgow have demonstrated a robotic guide dog designed to help partially sighted people navigate indoor spaces.
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Wang, Jianguo, Shiwei Lin, and Ang Liu. "Bioinspired Perception and Navigation of Service Robots in Indoor Environments: A Review." Biomimetics 8, no. 4 (August 7, 2023): 350. http://dx.doi.org/10.3390/biomimetics8040350.

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Biological principles draw attention to service robotics because of similar concepts when robots operate various tasks. Bioinspired perception is significant for robotic perception, which is inspired by animals’ awareness of the environment. This paper reviews the bioinspired perception and navigation of service robots in indoor environments, which are popular applications of civilian robotics. The navigation approaches are classified by perception type, including vision-based, remote sensing, tactile sensor, olfactory, sound-based, inertial, and multimodal navigation. The trend of state-of-art techniques is moving towards multimodal navigation to combine several approaches. The challenges in indoor navigation focus on precise localization and dynamic and complex environments with moving objects and people.
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Dahri, Fida Hussain, Ghulam E. Mustafa Abro, Nisar Ahmed Dahri, Asif Ali Laghari, and Zain Anwar Ali. "Advancing Robotic Automation with Custom Sequential Deep CNN-Based Indoor Scene Recognition." IECE Transactions on Intelligent Systematics 2, no. 1 (December 27, 2024): 14–26. https://doi.org/10.62762/tis.2025.613103.

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Indoor scene recognition poses considerable hurdles, especially in cluttered and visually analogous settings. Although several current recognition systems perform well in outside settings, there is a distinct necessity for enhanced precision in inside scene detection, particularly for robotics and automation applications. This research presents a revolutionary deep Convolutional Neural Network (CNN) model tailored with bespoke parameters to improve indoor picture comprehension. Our proprietary dataset consists of seven unique interior scene types, and our deep CNN model is trained to attain excellent accuracy in classification tasks. The model exhibited exceptional performance, achieving a training accuracy of 99%, a testing accuracy of 89.73%, a precision of 90.11%, a recall of 89.73%, and an F1-score of 89.79%. These findings underscore the efficacy of our methodology in tackling the intricacies of indoor scene recognition. This research substantially advances the domain of robotics and automation by establishing a more resilient and dependable framework for autonomous navigation and scene comprehension in GPS-denied settings, facilitating the development of more efficient and intelligent robotic systems.
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Caro, Luis, Javier Correa, Pablo Espinace, Daniel Langdon, Daniel Maturana, Ruben Mitnik, Sebastian Montabone, et al. "Indoor Mobile Robotics at Grima, PUC." Journal of Intelligent & Robotic Systems 66, no. 1-2 (July 20, 2011): 151–65. http://dx.doi.org/10.1007/s10846-011-9604-2.

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Frías, E., J. Balado, L. Díaz-Vilariño, and H. Lorenzo. "POINT CLOUD ROOM SEGMENTATION BASED ON INDOOR SPACES AND 3D MATHEMATICAL MORPHOLOGY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIV-4/W1-2020 (September 3, 2020): 49–55. http://dx.doi.org/10.5194/isprs-archives-xliv-4-w1-2020-49-2020.

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Abstract. Room segmentation is a matter of ongoing interesting for indoor navigation and reconstruction in robotics and AEC. While in robotics field, the problem room segmentation has been typically addressed on 2D floorplan, interest in enrichment 3D models providing more detailed representation of indoors has been growing in the AEC. Point clouds make available more realistic and update but room segmentation from point clouds is still a challenging topic. This work presents a method to carried out point cloud segmentation into rooms based on 3D mathematical morphological operations. First, the input point cloud is voxelized and indoor empty voxels are extracted by CropHull algorithm. Then, a morphological erosion is performed on the 3D image of indoor empty voxels in order to break connectivity between voxels belonging to adjacent rooms. Remaining voxels after erosion are clustered by a 3D connected components algorithm so that each room is individualized. Room morphology is retrieved by individual 3D morphological dilation on clustered voxels. Finally, unlabelled occupied voxels are classified according proximity to labelled empty voxels after dilation operation. The method was tested in two real cases and segmentation performance was evaluated with encouraging results.
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Jimenez Builes, Jovani Alberto, Gustavo Acosta Amaya, and Julián López Velásquez. "Autonomous navigation and indoor mapping for a service robot." Investigación e Innovación en Ingenierías 11, no. 2 (September 22, 2023): 28–38. http://dx.doi.org/10.17081/invinno.11.2.6459.

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Abstract Objective: Simultaneous Localization and Mapping (SLAM) is a quite common and interesting problem in mobile robotics. It is the basis of safe autonomous navigation of mobile robots and the entrance to new combined applications with a manipulator for instance. Method: In order to find a solution to the SLAM problem, the ROS middleware and the MRPT were selected. Autonomous navigation was tested using two methods, the MRPT navigation ROS package, which is a reactive navigation method based on Trajectory Parameter Space (TP-Space) transformations, and the ROS navigation stack, a standard for differential drive and holonomic wheeled robots. Results: To validate the advantages and disadvantages of both approaches, a mobile robot with strong kinematic constraints (Ackermann-steering-type) known as Summit was used. As an additional work, an application using the mobile robot Summit and a robotic manipulator (Powerball) was carried out, with the intention of picking and placing objects of the mobile robot, a widely spread application among service robotics, especially, in the area of industrial logistics. Conclusions: Finally, it is concluded that with the tests carried out with the robot, it was possible to demonstrate autonomous navigation, using the two mentioned methods.
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Tajti, Ferenc, Géza Szayer, Bence Kovács, and Mauricio A. P. Burdelis. "Mobile Robot Performance Analysis for Indoor Robotics." Periodica Polytechnica Civil Engineering 59, no. 2 (2015): 123–31. http://dx.doi.org/10.3311/ppci.7759.

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9

Vekhter, Joshua, and Joydeep Biswas. "Responsible Robotics: A Socio-Ethical Addition to Robotics Courses." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 15877–85. http://dx.doi.org/10.1609/aaai.v37i13.26885.

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We are witnessing a rapid increase in real-world autonomous robotic deployments in environments ranging from indoor homes and commercial establishments to large-scale urban areas, with applications ranging from domestic assistance to urban last-mile delivery. The developers of these robots inevitably have to make impactful design decisions to ensure commercially viability, but such decisions have serious real-world consequences. Unfortunately it is not uncommon for such projects to face intense bouts of social backlash, which can be attributed to a wide variety of causes, ranging from inappropriate technical design choices to transgressions of social norms and lack of community engagement. To better prepare students for the rigors of developing and deploying real-world robotics systems, we developed a Responsible Robotics teaching module, intended to be included in upper-division and graduate level robotics courses. Our module is structured as a role playing exercise which aims to equip students with a framework for navigating the conflicting goals of human actors which govern robots in the field. We report on instructor reflections and anonymous survey responses from offering our responsible robotics module in both a graduate-level, and an upper-division undergraduate robotics course at UT Austin. The responses indicate that students gained a deeper understanding of the socio-technical factors of real-world robotics deployments than they might have using self-study methods, and the students proactively suggested that such modules should be more broadly included in CS courses.
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MANO, Marsel, and Genci CAPI. "1A2-D03 Adaptive navigation of a brain controlled robotic wheelchair in an indoor environment(Rehabilitation Robotics and Mechatronics (2))." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2013 (2013): _1A2—D03_1—_1A2—D03_4. http://dx.doi.org/10.1299/jsmermd.2013._1a2-d03_1.

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Ghawana, T., M. Aleksandrov, and S. Zlatanova. "3D GEOSPATIAL INDOOR NAVIGATION FOR DISASTER RISK REDUCTION AND RESPONSE IN URBAN ENVIRONMENT." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4 (September 19, 2018): 49–57. http://dx.doi.org/10.5194/isprs-annals-iv-4-49-2018.

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<p><strong>Abstract.</strong> Disaster management for urban environments with complex structures requires 3D extensions of indoor applications to support better risk reduction and response strategies. The paper highlights the need for assessment and explores the role of 3D geospatial information and modeling regarding the indoor structure and navigational routes which can be utilized as disaster risk reduction and response strategy. The reviewed models or methods are analysed testing parameters in the context of indoor risk and disaster management. These parameters are level of detail, connection to outdoor, spatial model and network, handling constraints. 3D reconstruction of indoors requires the structural data to be collected in a feasible manner with sufficient details. Defining the indoor space along with obstacles is important for navigation. Readily available technologies embedded in smartphones allow development of mobile applications for data collection, visualization and navigation enabling access by masses at low cost. The paper concludes with recommendations for 3D modeling, navigation and visualization of data using readily available smartphone technologies, drones as well as advanced robotics for Disaster Management.</p>
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Mozos, Oscar Martinez, Francois Chollet, Kouji Murakami, Ken'ichi Morooka, Tokuo Tsuji, Ryo Kurazume, and Tsutomu Hasegawa. "Tracing Commodities in Indoor Environments for Service Robotics." IFAC Proceedings Volumes 45, no. 22 (2012): 71–76. http://dx.doi.org/10.3182/20120905-3-hr-2030.00046.

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NITTA, Yoshihiro, Daiki SASAKI, and Hiroaki SAITO. "Research on Indoor Temperature Measurement Using Robotics Technology." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2021 (2021): 2P2—A06. http://dx.doi.org/10.1299/jsmermd.2021.2p2-a06.

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14

ANDO, Takeshi, and Hiroyuki UEMATSU. "Delivering Innovation Using Indoor Autonomous Mobile Robotics Technology." Proceedings of Mechanical Engineering Congress, Japan 2019 (2019): S40111. http://dx.doi.org/10.1299/jsmemecj.2019.s40111.

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15

Zuo, X., F. Yang, Y. Liang, Z. Gang, F. Su, H. Zhu, and L. Li. "AN IMPROVED AUTONOMOUS EXPLORATION FRAMEWORK FOR INDOOR MOBILE ROBOTICS USING REDUCED APPROXIMATED GENERALIZED VORONOI GRAPHS." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2020 (August 3, 2020): 351–59. http://dx.doi.org/10.5194/isprs-annals-v-1-2020-351-2020.

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Abstract. In the field of autonomous navigation for robotics, one of the most challenging issues is to locate the Next-Best-View and to guide robotics through a previously unknown environment. Existing methods based on generalized Voronoi graphs (GVGs) have presented feasible solutions but require excessive computation to construct GVGs from metric maps, and the GVGs are usually redundant. This paper proposes a reduced approximated GVG (RAGVG), which provides a topological representation of the explored space with a smaller graph. To be specific, a fast and practical algorithm for constructing RAGVGs from metric maps is presented, and a RAGVG-based autonomous robotic exploration framework is designed and implemented. The proposed method for constructing RAGVGs is validated with two known common maps, while the RAGVG-based autonomous exploration framework is tested on two simulation and one real-world museum. The experimental results show that the proposed algorithm is efficient in constructing RAGVGs, and indicate that the mobile robot controlled by the RAGVG-based autonomous exploration framework, compared with famous frontiers-based method, reduced the total time by approximately 20% for the given tasks.
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Liu, Zhizhong, Weishen Yan, Jianguo Kou, and Zhida Li. "Collaboration and Management of Heterogeneous Robotic Systems for Road Network Construction, Management, and Maintenance under the Vision of “BIM + GIS” Technology." Journal of Robotics 2023 (April 12, 2023): 1–8. http://dx.doi.org/10.1155/2023/8259912.

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In modern society, intelligent spatial analysis services have been playing an important role in building smart cities. Especially, people’s spatial information needs have changed with the technological progress of times, from traditional two-dimensional appearance to three-dimensional interior. In this case, the construction of street networks is a key part of spatial information analysis research. Establishing an integrated indoor-outdoor street network data model with accurate information and complete attributes can provide strong technical support for indoor-outdoor navigation, emergency evacuation, and other application services. However, most of the current studies on internal and external road networks are independent of each other. This means that indoor road networks are mainly based on floor plans, which contain little information and do not represent the spatial relationships between floors. Indoor and outdoor links often suffer from poor connectivity and data loss. Heterogeneous robotics is a high degree of integration of intelligent control and platform control technologies. In a sense, it is a technology platform for connecting heterogeneous robotic populations. By developing effective collaboration and control strategies, tasks can be performed with the highest success rate and the lowest risk while respecting spatial and temporal constraints. As a result, the study of the coordination and management of heterogeneous robotic systems for road network construction, management, and maintenance is a topic of a great theoretical value and significance. Therefore, this paper proposes an intelligent road network construction method that incorporates BIM and GIS technologies. This study uses the building information model to enrich the information of indoor building components and combines the strong spatial analysis capability of outdoor areas with GIS road network technology to build an indoor road network and design a connection scheme for indoor and outdoor areas. After that, the construction of an integrated indoor-outdoor street network is completed, and the interaction between indoor as well as outdoor spatial data is very smooth.
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Potortì, Francesco, Filippo Palumbo, and Antonino Crivello. "Sensors and Sensing Technologies for Indoor Positioning and Indoor Navigation." Sensors 20, no. 20 (October 20, 2020): 5924. http://dx.doi.org/10.3390/s20205924.

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The last 10 years have seen enormous technical progress in the field of indoor positioning and indoor navigation; yet, in contrast with outdoor well-established GNSS solutions, no technology exists that is cheap and accurate enough for the general market. The potential applications of indoor localization are all-encompassing, from home to wide public areas, from IoT and personal devices to surveillance and crowd behavior applications, and from casual use to mission-critical systems. This special issue is focused on the recent developments within the sensors and sensing technologies for indoor positioning and indoor navigation networks domain. The papers included in this special issue provide useful insights to the implementation, modelling, and integration of novel technologies and applications, including location-based services, indoor maps and 3D building models, human motion monitoring, robotics and UAV, self-contained sensors, wearable and multi-sensor systems, privacy and security for indoor localization systems.
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Ismai, Abdul Halim, and Kazuhiko Terashima. "OS17-2 Optimization of Wireless Nodes Placement for Mobile Robot Indoor Localization(Robotics and Mechatronics (1),OS17 Robotics and mechatronics,APPLICATIONS)." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2015.14 (2015): 229. http://dx.doi.org/10.1299/jsmeatem.2015.14.229.

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Acosta-Amaya, Gustavo A., Deimer A. Miranda-Montoya, and Jovani A. Jimenez-Builes. "Lightweight Two-Layer Control Architecture for Human-Following Robot." Sensors 24, no. 23 (December 5, 2024): 7796. https://doi.org/10.3390/s24237796.

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(1) Background: Human detection and tracking are critical tasks for assistive autonomous robots, particularly in ensuring safe and efficient human–robot interaction in indoor environments. The increasing need for personal assistance among the elderly and people with disabilities has led to the development of innovative robotic systems. (2) Methods: This research presents a lightweight two-layer control architecture for a human-following robot, integrating a fuzzy behavior-based control system with low-level embedded controllers. The system uses an RGB-D sensor to capture distance and angular data, processed by a fuzzy controller to generate speed set-points for the robot’s motors. The low-level control layer was developed using pole placement and internal model control (IMC) methods. (3) Results: Experimental validation demonstrated that the proposed architecture enables the robot to follow a person in real time, maintaining the predefined following distance of 1.3 m in each of the five conducted trials. The IMC-based controller demonstrated superior performance compared to the pole placement controller across all evaluated metrics. (4) Conclusions: The proposed control architecture effectively addresses the challenges of human-following in indoor environments, offering a robust, real-time solution suitable for assistive robotics with limited computational resources. The system’s modularity and scalability make it a promising approach for future developments in personal assistance robotics.
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Basha, Mudasar, Munuswamy Siva Kumar, Mangali Chinna Chinnaiah, Siew-Kei Lam, Thambipillai Srikanthan, Janardhan Narambhatla, Hari Krishna Dodde, and Sanjay Dubey. "Hardware Schemes for Smarter Indoor Robotics to Prevent the Backing Crash Framework Using Field Programmable Gate Array-Based Multi-Robots." Sensors 24, no. 6 (March 7, 2024): 1724. http://dx.doi.org/10.3390/s24061724.

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The use of smart indoor robotics services is gradually increasing in real-time scenarios. This paper presents a versatile approach to multi-robot backing crash prevention in indoor environments, using hardware schemes to achieve greater competence. Here, sensor fusion was initially used to analyze the state of multi-robots and their orientation within a static or dynamic scenario. The proposed novel hardware scheme-based framework integrates both static and dynamic scenarios for the execution of backing crash prevention. A round-robin (RR) scheduling algorithm was composed for the static scenario. Dynamic backing crash prevention was deployed by embedding a first come, first served (FCFS) scheduling algorithm. The behavioral control mechanism of the distributed multi-robots was integrated with FCFS and adaptive cruise control (ACC) scheduling algorithms. The integration of multiple algorithms is a challenging task for smarter indoor robotics, and the Xilinx-based partial reconfiguration method was deployed to avoid computational issues with multiple algorithms during the run-time. These methods were coded with Verilog HDL and validated using an FPGA (Zynq)-based multi-robot system.
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Tagliavini, Luigi, Lorenzo Baglieri, Giovanni Colucci, Andrea Botta, Carmen Visconte, and Giuseppe Quaglia. "D.O.T. PAQUITOP, an Autonomous Mobile Manipulator for Hospital Assistance." Electronics 12, no. 2 (January 4, 2023): 268. http://dx.doi.org/10.3390/electronics12020268.

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The use of robotic technologies for caregiving and assistance has become a very interesting research topic in the field of robotics. Towards this goal, the researchers at Politecnico di Torino are developing robotic solutions for indoor assistance. This paper presents the D.O.T. PAQUITOP project, which aims at developing a mobile robotic assistant for the hospital environment. The mobile robot is composed of a custom omnidirectional platform, named PAQUITOP, a commercial 6 dof robotic arm, sensors for monitoring vital signs in patients, and a tablet to interact with the patient. To prove the effectiveness of this solution, preliminary tests were conducted with success in the laboratories of Politecnico di Torino and, thanks to the collaboration with the Onlus Fondazione D.O.T. and the medical staff of Molinette Hospital in Turin (Italy), at the hematology ward of Molinette Hospital.
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Cao, Junming, Xiting Zhao, and Sören Schwertfeger. "Large-Scale Indoor Visual–Geometric Multimodal Dataset and Benchmark for Novel View Synthesis." Sensors 24, no. 17 (September 6, 2024): 5798. http://dx.doi.org/10.3390/s24175798.

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The accurate reconstruction of indoor environments is crucial for applications in augmented reality, virtual reality, and robotics. However, existing indoor datasets are often limited in scale, lack ground truth point clouds, and provide insufficient viewpoints, which impedes the development of robust novel view synthesis (NVS) techniques. To address these limitations, we introduce a new large-scale indoor dataset that features diverse and challenging scenes, including basements and long corridors. This dataset offers panoramic image sequences for comprehensive coverage, high-resolution point clouds, meshes, and textures as ground truth, and a novel benchmark specifically designed to evaluate NVS algorithms in complex indoor environments. Our dataset and benchmark aim to advance indoor scene reconstruction and facilitate the creation of more effective NVS solutions for real-world applications.
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Khan, Sultan Daud, and Kamal M. Othman. "Indoor Scene Classification through Dual-Stream Deep Learning: A Framework for Improved Scene Understanding in Robotics." Computers 13, no. 5 (May 14, 2024): 121. http://dx.doi.org/10.3390/computers13050121.

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Indoor scene classification plays a pivotal role in enabling social robots to seamlessly adapt to their environments, facilitating effective navigation and interaction within diverse indoor scenes. By accurately characterizing indoor scenes, robots can autonomously tailor their behaviors, making informed decisions to accomplish specific tasks. Traditional methods relying on manually crafted features encounter difficulties when characterizing complex indoor scenes. On the other hand, deep learning models address the shortcomings of traditional methods by autonomously learning hierarchical features from raw images. Despite the success of deep learning models, existing models still struggle to effectively characterize complex indoor scenes. This is because there is high degree of intra-class variability and inter-class similarity within indoor environments. To address this problem, we propose a dual-stream framework that harnesses both global contextual information and local features for enhanced recognition. The global stream captures high-level features and relationships across the scene. The local stream employs a fully convolutional network to extract fine-grained local information. The proposed dual-stream architecture effectively distinguishes scenes that share similar global contexts but contain different localized objects. We evaluate the performance of the proposed framework on a publicly available benchmark indoor scene dataset. From the experimental results, we demonstrate the effectiveness of the proposed framework.
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Yao, M. M., X. M. Li, W. X. Wang, L. F. Xie, and S. J. Tang. "SEMANTIC SEGMENTATION OF INDOOR 3D POINT CLOUDS BY JOINT OPTIMIZATION OF GEOMETRIC FEATURES AND NEURAL NETWORKS." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W2-2022 (October 14, 2022): 305–10. http://dx.doi.org/10.5194/isprs-annals-x-4-w2-2022-305-2022.

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Abstract. Indoor navigation, indoor robotics, and other deep applications of interior space can be realized through semantic segmentation of 3D point clouds. We propose a semantic segmentation method for point clouds that uses geometric features of point clouds and neural networks to address the problem of incomplete and inconsistent segmentation objectives in existing semantic segmentation methods. Using neural networks, semantic labels are extracted from indoor structural information as the first step. The paper proposes a probabilistic model to cross-validate the initial segmentation results with the segmentation results of geometric features to achieve joint optimization of the results for semantic segmentation. Three sets of indoor point clouds data from simple to complex indoor scenes are used to test the accuracy and validity of the segmentation method proposed in this paper. The experimental results demonstrate that the method proposed in this paper can effectively improve the semantic segmentation accuracy of indoor 3D point clouds.
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Martinez, Fredy, Holman Montiel, and Fernando Martinez. "A novel visual tracking scheme for unstructured indoor environments." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 6 (December 1, 2023): 6216. http://dx.doi.org/10.11591/ijece.v13i6.pp6216-6227.

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In the ever-expanding sphere of assistive robotics, the pressing need for advanced methods capable of accurately tracking individuals within unstructured indoor settings has been magnified. This research endeavours to devise a realtime visual tracking mechanism that encapsulates high performance attributes while maintaining minimal computational requirements. Inspired by the neural processes of the human brain’s visual information handling, our innovative algorithm employs a pattern image, serving as an ephemeral memory, which facilitates the identification of motion within images. This tracking paradigm was subjected to rigorous testing on a Nao humanoid robot, demonstrating noteworthy outcomes in controlled laboratory conditions. The algorithm exhibited a remarkably low false detection rate, less than 4%, and target losses were recorded in merely 12% of instances, thus attesting to its successful operation. Moreover, the algorithm’s capacity to accurately estimate the direct distance to the target further substantiated its high efficacy. These compelling findings serve as a substantial contribution to assistive robotics. The proficient visual tracking methodology proposed herein holds the potential to markedly amplify the competencies of robots operating in dynamic, unstructured indoor settings, and set the foundation for a higher degree of complex interactive tasks.
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Ismail, Zool H., and Iksan Bukhori. "Efficient Detection of Robot Kidnapping in Range Finder-Based Indoor Localization Using Quasi-Standardized 2D Dynamic Time Warping." Applied Sciences 11, no. 4 (February 9, 2021): 1580. http://dx.doi.org/10.3390/app11041580.

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This paper proposes an augmented online approach to detect kidnapping events within range-finder-based indoor localization. The method is specifically designed for an Internet of Things (IoT)-Aided Robotics Platform that enables the system to detect kidnapping across all time instances of an indoor mobile robotic operation with high accuracy and maintain a high accuracy in the face of relocalization failures. The approach is based on similarity degree of geometry shape of the environment obtained from range scan data between two consecutive time instances. The proposed approach named Quasi-Standardized Two-Dimensional Dynamic Time Warping (QS-2DDTW) is based on the Multidimensional Dynamic Time Warping (MD-DTW) with homogeneity variance test imbued in it. A series of simulations are preformed against maximum current weight, measurement entropy, and the four metrics in metric based detector. The result shows that the proposed method yields high performance in terms of its ability to distinguish kidnapping condition from normal condition and that it has low dependency towards relocalization process, thus ensures the accuracy of detection is not disturbed by relocalization.
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Kwon, Ki-Hyeon, Si-Byung Nam, and Se-Hun Lee. "Collision Avoidance for Indoor Mobile Robotics using Stereo Vision Sensor." Journal of the Korea Academia-Industrial cooperation Society 14, no. 5 (May 31, 2013): 2400–2405. http://dx.doi.org/10.5762/kais.2013.14.5.2400.

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Jung, Tae-Won, Chi-Seo Jeong, Soon-Chul Kwon, and Kye-Dong Jung. "Point-Graph Neural Network Based Novel Visual Positioning System for Indoor Navigation." Applied Sciences 11, no. 19 (October 2, 2021): 9187. http://dx.doi.org/10.3390/app11199187.

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Indoor localization is a basic element in location-based services (LBSs), including seamless indoor and outdoor navigation, location-based precision marketing, spatial recognition in robotics, augmented reality, and mixed reality. The popularity of LBSs in the augmented reality and mixed reality fields has increased the demand for a stable and efficient indoor positioning method. However, the problem of indoor visual localization has not been appropriately addressed, owing to the strict trade-off between accuracy and cost. Therefore, we use point cloud and RGB characteristic information for the accurate acquisition of three-dimensional indoor space. The proposed method is a novel visual positioning system (VPS) capable of determining the user’s position by matching the pose information of the object estimated by the improved point-graph neural network (GNN) with the pose information label of a voxel database object addressed in predefined voxel units. We evaluated the performance of the proposed system considering a stationary object in indoor space. The results verify that high positioning accuracy and direction estimation can be efficiently achieved. Thus, spatial information of indoor space estimated using the proposed novel VPS can aid in indoor navigation.
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Leandro Vizzotto, Fábio, Marcos D'Addio de Moura, Vinicius Carbonezi de Souza, Cides Semprebom Bezerra, Guilherme Ribeiro Sales, Valentino Corso, Luiz Eduardo Pita Mercês Almeida, and Douglas Henrique Siqueira Abreu. "Case Study of Deep Learning Methods for Depth Estimation in Indoor Ground Robotics." Revista de Informática Teórica e Aplicada 32, no. 1 (February 20, 2025): 166–72. https://doi.org/10.22456/2175-2745.143443.

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Depth estimation is the computer vision task that assigns a distance between the camera and each pixel in an image. This paper focuses on monocular metric depth estimation in videos, which infers a distance in metric units using a single RGB camera. Considering its applications, robotics systems and environmental mapping arise as practical areas that can make extensive usage of these techniques. As a case study for indoor robotics, the ICL ground robot dataset obtained by video footage in graphic simulation was used for experiments. A comparison was made considering the results and requirements of data acquisition needed for different deep learning models, presenting self-supervised and supervised methods available in literature and being the first work to present a depth estimation benchmark for the chosen dataset.
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Chung, Chan-Jin. "Robofest – A Playful Learning Environment Through Autonomous Robotics." Pädi Boletín Científico de Ciencias Básicas e Ingenierías del ICBI 7, Especial (September 4, 2019): 1–3. http://dx.doi.org/10.29057/icbi.v7iespecial.4770.

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Robofest [1, 2, 3] is a student centered indoor autonomous robotics competition based on PBL (Problem-Based Learning) [4] and CBL (Competition-Based Learning) [5] paradigms. Its primary mission is to generate excitement & interest among young people for Science, Technology, Engineering, and Mathematics (STEM) through robotics projects integrating STEM subjects and Computer Science. Secondly, Robofest provides playful environment for students to develop well-rounded core value skills such as teamwork, leadership, creativity/entrepreneurship, communication and problem solving skills. Our third mission is to prepare youth to excel in higher education and technological careers for the future.
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Abdul-Samed, Baqir, and Ammar Aldair. "Outdoor & Indoor Quadrotor Mission." 3D SCEEER Conference sceeer, no. 3d (July 1, 2020): 1–12. http://dx.doi.org/10.37917/ijeee.sceeer.3rd.01.

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The last few years Quadrotor became an important topic, many researches have implemented and tested concerning that topic. Quadrotor also called an unmanned Aerial Vehicle (UAV), it's highly used in many applications like security, civil applications, aid, rescue and a lot of other applications. It’s not a conventional helicopter because of small size, low cost and the ability of vertical and takeoff landing (VTOL). The models kept an eye on quadrotors were presented, the advancement of this new kind of air vehicle is hindered for a very long while because of different reasons, for example, mechanical multifaceted nature, enormous size and weight, and challenges in charge particularly. Just as of late a lot of interests and endeavors have been pulled in on it; a quadrotor has even become a progressively discretionary vehicle for useful application. Quadrotor can be used in variable, different , outdoor and indoor missions; these missions should be implemented with high value of accuracy and quality. In this work two scenarios suggested for different two missions. First mission the quadrotor will be used to reach different goals in the simulated city for different places during one flight using path following algorithm. The second mission will be an indoor arrival mission, during that mission quadrotor will avoid obstacles by using only Pure pursuit algorithm (PPA). To show the benefit of using the new strategy it will compare with a victor field histogram algorithm (VFH) which is used widely in robotics for avoiding obstacles, the comparison will be in terms of reaching time and distance of reaching the goal. The Gazebo Simulator (GS) is used to visualize the movement of the quadrotor. The gazebo has another preferred position it helps to show the motion development of the quadrotor without managing the mathematical model of the quadrotor. The Robotic Operating System (ROS) is used to transfer the data between the MATLAB Simulink program and the Gazebo Simulator. The diversion results show that, the proposed mission techniques win to drive the quarter on the perfect route similarly at the limit with regards to the quadrotor to go without hitting any obstacle in the perfect way.
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de Ronde, Willis, Natasha Botha, Beatrice van Eden, and Lerato Tshabalala. "Material selection and optimisation of a 3D-printed indoor aerial robotics platform." MATEC Web of Conferences 406 (2024): 04016. https://doi.org/10.1051/matecconf/202440604016.

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Both aerial robotic platforms and additive manufacturing (AM) have become more affordable to consumers. Indoor aerial robotic platforms are typically small and lightweight, while AM is renowned for creating small, high-strength prototypes and components. This paper discusses the material selection and structural optimisation of a 3D-printed indoor aerial robotic platform. Three commonly used AM materials were compared using finite element analysis (FEA): acrylonitrile butadiene styrene (ABS), polyethylene terephthalate glycol (PETG), and Nylon. It was found that Nylon offered the best performance in terms of the strength-to-weight ratio. The aerial robotic frame was optimised using an iterative design approach and previous knowledge with regards to the breaks observed during flight crashes. A dynamic FEA was performed to simulate a drop test from a height of one meter to compare the optimised design with the previous frame design. It was found that the improvements in the redesign have led to a 13.67 % decrease in weight and a 11.78 % decrease stress of the aerial robotic frame. This not only demonstrates the effectiveness of design optimisation, but also highlights the commitment to producing more efficient, reliable and sustainable designs.
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C., Ujjwal K., and Jacques Chodorowski. "A Case Study of Adding Proactivity in Indoor Social Robots Using Belief–Desire–Intention (BDI) Model." Biomimetics 4, no. 4 (November 20, 2019): 74. http://dx.doi.org/10.3390/biomimetics4040074.

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The rise of robots and robotics has proved to be a benefaction to humankind in different aspects. Robotics evolved from a simple button, has seen massive development over the years. Consequently, it has become an integral part of human life as robots are used for a wide range of applications ranging from indoor uses to interplanetary missions. Recently, the use of social robots, in commercial indoor spaces to offer help or social interaction with people, has been quite popular. As such, taking the increasing use of social robots into consideration, many works have been carried out to develop the robots to make them capable of acting like humans. The notion behind this development is the need for robots to offer services without being asked. Social robots should think more like humans and suggest possible and suitable actions by analyzing the environment where they are. Belief–desire–intention (BDI) is one of the most popular models for developing rational agents based on how humans act based on the information derived from an environment. As such, this work defines a foundation architecture to integrate a BDI framework into a social robot to add “act like a human” feature for proactive behaviors. The work validates the proposed architecture by developing a vision-based proactive action using the PROFETA BDI framework in an indoor social robot, Waldo, operated by the robot operating system (ROS).
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Liu, Hanyu, Yanhan Zeng, Ruguo Li, and Huajie Huang. "A high-accuracy indoor positioning system based on UWB." MATEC Web of Conferences 173 (2018): 01021. http://dx.doi.org/10.1051/matecconf/201817301021.

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In this paper, a high-accuracy indoor positioning system based on the ultra-wideband (UWB) technique is proposed. The proposed system uses a simple ranging process to obtain the distance between the mobile node and the fixed base stations. Besides, an improved time of arrival (ToA) algorithm with Kalman filtering is proposed to improve the positioning accuracy. Measurements have been performed in the real indoor 13m*7.6m environment with many obstacles and the root-mean-square error (RMSE) is less than 0.3m. The proposed system offers a wide range of application in robotics, industrial automation, post-sorting system and so on.
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Fernando, Gimo C., Tinghao Qi, Edmund V. Ndimbo, Assefa Tesfay Abraha, and Bang Wang. "On Fusing Wireless Fingerprints with Pedestrian Dead Reckoning to Improve Indoor Localization Accuracy." Sensors 25, no. 5 (February 20, 2025): 1294. https://doi.org/10.3390/s25051294.

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Accurate indoor positioning remains a critical challenge due to the limitations of single-source systems, such as signal instability and environmental obstructions. This study introduces a multi-source fusion positioning algorithm that integrates inertial sensors and signal fingerprints to address these issues. Using a weighted fusion method, the algorithm employs pedestrian dead reckoning (PDR) for trajectory tracking and combines its outputs with wireless signal fingerprints. Experimental evaluations conducted on diverse trajectories reveal significant improvements in accuracy, achieving a 35.3% enhancement over wireless-only systems and a 71.4% improvement compared to standalone PDR. The proposed method effectively balances computational efficiency and accuracy, demonstrating robustness in complex and dynamic indoor environments. These findings establish the algorithm’s potential for practical applications in navigation, robotics, and Industry 4.0, where precise indoor localization is essential.
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Chen, Yujin, Ruizhi Chen, Mengyun Liu, Aoran Xiao, Dewen Wu, and Shuheng Zhao. "Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free." Sensors 18, no. 8 (August 16, 2018): 2692. http://dx.doi.org/10.3390/s18082692.

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Indoor localization is one of the fundamentals of location-based services (LBS) such as seamless indoor and outdoor navigation, location-based precision marketing, spatial cognition of robotics, etc. Visual features take up a dominant part of the information that helps human and robotics understand the environment, and many visual localization systems have been proposed. However, the problem of indoor visual localization has not been well settled due to the tough trade-off of accuracy and cost. To better address this problem, a localization method based on image retrieval is proposed in this paper, which mainly consists of two parts. The first one is CNN-based image retrieval phase, CNN features extracted by pre-trained deep convolutional neural networks (DCNNs) from images are utilized to compare the similarity, and the output of this part are the matched images of the target image. The second one is pose estimation phase that computes accurate localization result. Owing to the robust CNN feature extractor, our scheme is applicable to complex indoor environments and easily transplanted to outdoor environments. The pose estimation scheme was inspired by monocular visual odometer, therefore, only RGB images and poses of reference images are needed for accurate image geo-localization. Furthermore, our method attempts to use lightweight datum to present the scene. To evaluate the performance, experiments are conducted, and the result demonstrates that our scheme can efficiently result in high location accuracy as well as orientation estimation. Currently the positioning accuracy and usability enhanced compared with similar solutions. Furthermore, our idea has a good application foreground, because the algorithms of data acquisition and pose estimation are compatible with the current state of data expansion.
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Shamsudin, Abu Ubaidah, Puteri Alisha Balqis Mohd Sharif, Zubair Adil Soomro, Ruzairi Abdul Rahim, Ahmad Athif Mohd Faudzi, Wan Nurshazwani Wan Zakaria, Mohamad Heerwan Peeie, and Carl John Salaan. "Autonomous Navigation Robot using Slam and Path Planning Based on a Single RP-LIDAR." Journal of Advanced Research in Applied Sciences and Engineering Technology 53, no. 2 (October 7, 2024): 161–69. https://doi.org/10.37934/araset.53.2.161169.

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This paper proposes an autonomous robotics system based on a single LIDAR sensor for delivering goods. There are needs to deliver goods such as medical supplies to the hospital and document in office. Although this task is simple, the delivery task is time consuming, tedious, and boring. In this regard, autonomous robotic systems could be a suitable solution. Here-in, we proposed the use of a single RP-LIDAR sensor in a robotic system that is sufficient for delivering goods in indoor environment. The robotic system uses autonomous navigation module which consists of simultaneous localization and mapping (SLAM) of the environment, and path planning for the robot to move safely from its current location to a desired position. In the context of SLAM, Hector SLAM method was opted due to its capability to harness sufficient data from a single LIDAR sensor. When it comes to path planning, the system employs a two-pronged approach. First, a global planner, utilizing the A* algorithm, facilitates long-distance trajectory planning while accounting for static obstacles. Second, a local planner, employing the Trajectory Rollout and Dynamic Window Approach (DWA), ensures real-time obstacle avoidance in the presence of moving obstacles. The approach was evaluated in a laboratory environment (11 meter by 8 meter) and successfully built a path that avoided obstacles in both static and dynamic conditions. This approach for autonomous robotics using a single RP-LIDAR sensor has the potential to revolutionize the delivery industry and other sectors that require autonomous navigation.
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Assali, M., G. Pipelidis, V. Podolskiy, D. Iwaszczuk, L. Heinen, and M. Gerndt. "QUANTIFYING THE QUALITY OF INDOOR MAPS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W13 (June 5, 2019): 739–45. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w13-739-2019.

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<p><strong>Abstract.</strong> Indoor maps are required for multiple applications, such as, navigation, building maintenance and robotics. One of common methods for map generation is laser scanning. In such maps, not only geometry of the map is of interest, but also its quality. This study aims at developing methods for real-time generation of indoor maps using features extracted from pointclouds obtained by a robot with their simultaneous quality assessment. We investigate, how this quality can be quantified for feature-based maps. First, we introduce a method for modeling 2D maps into 3D models that enable their usage for localization. Second, we review and evaluate a number of algorithms that can enable us to address features in a map. Hence, we enable the generation of objects from a pointcloud that has been sensed. Finally, we study several aspects of the map quality and we formalize them into metrics that can be applied to quantify their quality.</p>
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Pitkäaho, Tomi, Tero Kaarlela, Sakari Pieskä, and Sami Sarlin. "Indoor positioning, artificial intelligence and digital twins for enhanced robotics safety." IFAC-PapersOnLine 54, no. 1 (2021): 540–45. http://dx.doi.org/10.1016/j.ifacol.2021.08.062.

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Gregory, Calvin, and Andrew Vardy. "microUSV: A low-cost platform for indoor marine swarm robotics research." HardwareX 7 (April 2020): e00105. http://dx.doi.org/10.1016/j.ohx.2020.e00105.

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41

Bulanon, Duke M., Colton Burr, Marina DeVlieg, Trevor Braddock, and Brice Allen. "Development of a Visual Servo System for Robotic Fruit Harvesting." AgriEngineering 3, no. 4 (October 28, 2021): 840–52. http://dx.doi.org/10.3390/agriengineering3040053.

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One of the challenges in the future of food production, amidst increasing population and decreasing resources, is developing a sustainable food production system. It is anticipated that robotics will play a significant role in maintaining the food production system, specifically in labor-intensive operations. Therefore, the main goal of this project is to develop a robotic fruit harvesting system, initially focused on the harvesting of apples. The robotic harvesting system is composed of a six-degrees-of-freedom (DOF) robotic manipulator, a two-fingered gripper, a color camera, a depth sensor, and a personal computer. This paper details the development and performance of a visual servo system that can be used for fruit harvesting. Initial test evaluations were conducted in an indoor laboratory using plastic fruit and artificial trees. Subsequently, the system was tested outdoors in a commercial fruit orchard. Evaluation parameters included fruit detection performance, response time of the visual servo, and physical time to harvest a fruit. Results of the evaluation showed that the developed visual servo system has the potential to guide the robot for fruit harvesting.
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Goel, Keshav. "Automated Air Purifier Robot." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (May 31, 2024): 4052–55. http://dx.doi.org/10.22214/ijraset.2024.62468.

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Abstract: Growing air pollution has become a major worry for all countries since the advent of industrialization and modernity. Each person's health is affected differently by air pollution, which can lead to serious consequences. The largest environmental health concern in the world is air pollution, particularly indoor air pollution, which is connected to over 4 million deaths a year from conditions like lung cancer, ischemic heart disease, stroke, and chronic obstructive pulmonary disease.. The air inside our homes and workplaces can become contaminated with dangerous pollutants as cities and industries grow. This is where air purifiers become crucial, and new developments have introduced autonomous air purifier robots as a new player in the field. These robots are not your normal stationary air purifiers; instead, they move around and clean the air using cutting-edge technologies like robotics, artificial intelligence, and sensors. These robot air purifiers are more dynamic than typical air purifiers because they can adjust to changing indoor settings. The objective of this review paper is to examine autonomous air purifier robots, including their history, essential technological elements, uses, and possible effects on indoor air quality. We want to comprehend the benefits, constraints, and distinct issues these robotic solutions tackle when compared to traditional air purifiers by looking at the state of the art. We will also talk about user experiences, upcoming trends, and the moral issues surrounding the use of these self-governing systems
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Ridlwan, Hasvienda Mohammad, Sonki Prasetya, and Musli Min. "2D Mapping Lingkungan Indoor Menggunakan Lidar dan ROS untuk Mobile Robot." Jurnal Mekanik Terapan 3, no. 2 (August 31, 2022): 60–65. http://dx.doi.org/10.32722/jmt.v3i2.4285.

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Currently, the application of control systems has been applied in various scientific fields including mechatronics and robotics. Applications in the branch of robotics are also growing day by day not only with conventional controls but also with intelligent systems. An autonomous robot in carrying out certain missions in an unknown environment requires information about the location itself and the environment through the map. A process to identify a position without a map is called a localization function on the robot. Mobile robots building maps and localization are two fundamental tasks when mobile robots work in indoor environments. With 2D laser scanning (LiDAR) data obtained in real-time, the robot can calculate the area of ​​all empty spaces in a room, then can choose the center of the room as its position for map building. The objective of this research is to implement a two-dimensional mapping method using LiDAR. The algorithm used in this study is the Gmapping Technique on ROS. The main purpose of this research is to map mobile robots with LIDAR sensors using the Robot Operating System for navigation and positioning of mobile robots. Through the actual experimental results, the mobile robot will move with a 2-dimensional mapping process.
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44

Wu, Lei. "Analysis of Indoor Path Planning Techniques for Wheeled Mobile Robots." Highlights in Science, Engineering and Technology 52 (July 4, 2023): 208–17. http://dx.doi.org/10.54097/hset.v52i.8891.

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Path planning is crucial for autonomous mobile robots to navigate in unknown settings. Achieving autonomous navigation in indoor environments without collisions with static or dynamic obstacles, reaching the destination quickly, and meeting the requirements of specific work scenarios are essential challenges. Over the years, various path planning algorithms have been proposed in the literature, each with advantages and limitations. This paper provides a brief overview of advanced indoor path planning algorithms for wheeled mobile robots, listing classical algorithms as well as recent advancements. These algorithms' fundamental principles, features, and computational complexities are discussed and analyzed. Furthermore, this paper highlights some challenges and open research questions in path planning. This survey aims to provide readers with a concise review of existing indoor wheeled mobile robot path planning techniques and their applications, to inspire future research in this vital field of robotics.
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Liu, Yanbaihui. "Localization and Navigation System for Indoor Mobile Robot." Highlights in Science, Engineering and Technology 43 (April 14, 2023): 198–206. http://dx.doi.org/10.54097/hset.v43i.7420.

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Visually impaired people usually find it hard to travel independently in many public places such as airports and shopping malls due to the problems of obstacle avoidance and guidance to the desired location. Therefore, in the highly dynamic indoor environment, how to improve indoor navigation robot localization and navigation accuracy so that they guide the visually impaired well becomes a problem. One way is to use visual SLAM. However, typical visual SLAM either assumes a static environment, which may lead to less accurate results in dynamic environments or assumes that the targets are all dynamic and removes all the feature points above, sacrificing computational speed to a large extent with the available computational power. This paper seeks to explore marginal localization and navigation systems for indoor navigation robotics. The proposed system is designed to improve localization and navigation accuracy in highly dynamic environments by identifying and tracking potentially moving objects and using vector field histograms for local path planning and obstacle avoidance. The system has been tested on a public indoor RGB-D dataset, and the results show that the new system improves accuracy and robustness while reducing computation time in highly dynamic indoor scenes.
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46

Bao, Yin, Dylan S. Shah, and Lie Tang. "3D Perception-Based Collision-Free Robotic Leaf Probing for Automated Indoor Plant Phenotyping." Transactions of the ASABE 61, no. 3 (2018): 859–72. http://dx.doi.org/10.13031/trans.12653.

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Abstract. Various instrumentation devices for plant physiology studies, such as spectrometers, chlorophyll fluorometers, and Raman spectroscopy sensors, require accurate placement of their sensor probes toward the leaf surface to meet specific requirements of probe-to-target distance and orientation. In this work, a Kinect V2 sensor, a high-precision 2D laser profilometer, and a six-axis robotic manipulator were used to automate the leaf probing task. The relatively wide field of view and high resolution of the Kinect V2 allowed rapid capture of the full 3D environment in front of the robot. The location and size of each plant were estimated by k-means clustering where k was the user-defined number of plants. A real-time collision-free motion planning framework based on probabilistic roadmaps was adapted to maneuver the robotic manipulator without colliding with the plants. Each plant was scanned from the top with the short-range profilometer to obtain high-precision 3D point cloud data. Potential leaf clusters were extracted by a 3D region growing segmentation scheme. Each leaf segment was further partitioned into small patches by a voxel cloud connectivity segmentation method. Only the patches with low root mean square errors of plane fitting were used to compute leaf probing poses of the robot. Experiments conducted inside a growth chamber mockup showed that the developed robotic leaf probing system achieved an average motion planning time of 0.4 s with an average end-effector travel distance of 1.0 m. To examine the probing accuracy, a square surface was scanned at different angles, and its centroid was probed perpendicularly. The average absolute probing errors of distance and angle were 1.5 mm and 0.84°, respectively. These results demonstrate the utility of the proposed robotic leaf probing system for automated non-contact deployment of spectroscopic sensor probes for indoor plant phenotyping under controlled environmental conditions. Keywords: 3D perception, Agricultural robotics, Leaf probing, Motion planning, Plant phenotyping.
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Hensel, Stefan, Marin B. Marinov, and Markus Obert. "3D LiDAR Based SLAM System Evaluation with Low-Cost Real-Time Kinematics GPS Solution." Computation 10, no. 9 (September 4, 2022): 154. http://dx.doi.org/10.3390/computation10090154.

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Positioning mobile systems with high accuracy is a prerequisite for intelligent autonomous behavior, both in industrial environments and in field robotics. This paper describes the setup of a robotic platform and its use for the evaluation of simultaneous localization and mapping (SLAM) algorithms. A configuration using a mobile robot Husky A200, and a LiDAR (light detection and ranging) sensor was used to implement the setup. For verification of the proposed setup, different scan matching methods for odometry determination in indoor and outdoor environments are tested. An assessment of the accuracy of the baseline 3D-SLAM system and the selected evaluation system is presented by comparing different scenarios and test situations. It was shown that the hdl_graph_slam in combination with the LiDAR OS1 and the scan matching algorithms FAST_GICP and FAST_VGICP achieves good mapping results with accuracies up to 2 cm.
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Huang, H. S., S. J. Tang, W. X. Wang, X. M. Li, and R. Z. Guo. "FROM BIM TO POINTCLOUD: AUTOMATIC GENERATION OF LABELED INDOOR POINTCLOUD." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B5-2022 (June 2, 2022): 73–78. http://dx.doi.org/10.5194/isprs-archives-xliii-b5-2022-73-2022.

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Abstract. With the development of deep learning technology, a large number of indoor spatial applications, such as robotics and indoor navigation, have raised higher data requirements for indoor semantic model. However, creating deep learning classifiers requires a large number of labeled datasets, and the collection of such datasets requires a lot of manually labeling proces, which is labor-intensive and time-consuming. In this paper, we propose a method to automatically create 3D point clouds datasets with indoor semantic labels based on parametric BIM model. First, a automatic BIM generation method is proposed through simulating the structure of interior space Secondly, we use a viewpoint-guided labeled point cloud generation method to generate synthetic 3D point clouds with different labels, color information. Especially, noise are also simulated with a gaussian model. As shown in the experiments, the point cloud data with labels can be quickly obtained from existing BIM models, which will largely reduce the complexity of data labeling and improve efficiency. These simulated data can be used in the deep learning training process and improve the semantic segmentation accuracy.
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Surendran, Ranjini, Ines Chihi, J. Anitha, and D. Jude Hemanth. "Indoor Scene Recognition: An Attention-Based Approach Using Feature Selection-Based Transfer Learning and Deep Liquid State Machine." Algorithms 16, no. 9 (September 8, 2023): 430. http://dx.doi.org/10.3390/a16090430.

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Scene understanding is one of the most challenging areas of research in the fields of robotics and computer vision. Recognising indoor scenes is one of the research applications in the category of scene understanding that has gained attention in recent years. Recent developments in deep learning and transfer learning approaches have attracted huge attention in addressing this challenging area. In our work, we have proposed a fine-tuned deep transfer learning approach using DenseNet201 for feature extraction and a deep Liquid State Machine model as the classifier in order to develop a model for recognising and understanding indoor scenes. We have included fuzzy colour stacking techniques, colour-based segmentation, and an adaptive World Cup optimisation algorithm to improve the performance of our deep model. Our proposed model would dedicatedly assist the visually impaired and blind to navigate in the indoor environment and completely integrate into their day-to-day activities. Our proposed work was implemented on the NYU depth dataset and attained an accuracy of 96% for classifying the indoor scenes.
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Venture, Gentiane. "Special Issue on Developments and Learning from the World Robot Challenge." Journal of Robotics and Mechatronics 35, no. 1 (February 20, 2023): 7. http://dx.doi.org/10.20965/jrm.2023.p0007.

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The World Robot Challenge was organized to stimulate and gather findings in robotics and their applications to three important fields: Industrial Robotics, Service Robotics, and Disaster Robotics, which constituted three categories for the competition. Researchers and professionals from all over the world in the field of robotics were provided a unique opportunity to showcase their work by solving unprecedented problems, on the spot. Computer vision, robust control, navigation, and manipulation are some of the key elements to create robots that could solve the challenges proposed during the competition. This special issue provides some examples of the work developed and performed in that aim, that could serve for the future of robotics applications in the three designated fields but not only. The three first papers are the results of the convenience store challenge for Service Robotics. In this challenge, identifying objects and pose estimation of object are crucial for the acquisition of knowledge and robot manipulation, shelfing and logistics, as detailed in the first two papers. Robot’s navigation is also paramount for cleaning and operating in the human environment, such is the topic of the third paper. The Industrial Robotics challenge focuses on parts pick and place and assembly of complex elements. The fourth and 5th papers of this special issue deal with this topic. Finally the Disaster Robotics challenge presents the more versatile components as a disaster area may be of multiform and of very complex nature, depending of the nature of the disaster, whether it is indoor or outdoor etc. The last three papers of the special issue deal with such a various nature for plant inspection, for the control of robots in disaster area, or the rescue robot in confined spaces such as tunnels. We hope this collection of work carefully selected, will provide the reader with insights for the future developments of robots.
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