Dissertations / Theses on the topic 'Robotic appearances'
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1
Linegar, Chris. "Vision-only localisation under extreme appearance change." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:608762bd-5608-4e50-ab7b-da454dd52887.
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Robust localisation is a key requirement for autonomous vehicles. However, in order to achieve widespread adoption of this technology, we also require this function to be performed using low-cost hardware. Cameras are appealing due to their information-rich image content and low cost; however, camera-based localisation is difficult because of the problem of appearance change. For example, in outdoor en- vironments the appearance of the world can change dramatically and unpredictably with variations in lighting, weather, season and scene structure. We require autonomous vehicles to be robust under these challenging environmental conditions. This thesis presents Dub4, a vision-only localisation system for autonomous vehicles. The system is founded on the concept of experiences, where an "experience" is a visual memory which models the world under particular conditions. By allowing the system to build up and curate a map of these experiences, we are able to handle cyclic appearance change (lighting, weather and season) as well as adapt to slow structural change. We present a probabilistic framework for predicting which experiences are most likely to match successfully with the live image at run-time, conditioned on the robot's prior use of the map. In addition, we describe an unsupervised algorithm for detecting and modelling higher-level visual features in the environment for localisation. These features are trained on a per-experience basis and are robust to extreme changes in appearance, for example between rain and sun, or day and night. The system is tested on over 1500km of data, from urban and off-road environments, through sun, rain, snow, harsh lighting, at different times of the day and night, and through all seasons. In addition to this extensive offline testing, Dub4 has served as the primary localisation source on a number of autonomous vehicles, including the Oxford University's RobotCar, the 2016 Shell Eco-Marathon, the LUTZ PathFinder Project in Milton Keynes, and the GATEway Project in Greenwich, London.
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Garg, Sourav. "Robust visual place recognition under simultaneous variations in viewpoint and appearance." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/134410/1/Sourav%20Garg%20Thesis.pdf.
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This thesis explores the problem of visual place recognition and localization for a mobile robot, particularly dealing with the challenges of simultaneous variations in scene appearance and camera viewpoint. The proposed methods draw inspiration from humans and make use of semantic cues to represent places. This approach enables effective place recognition from similar or opposing viewpoints, despite variations in scene appearance caused by different times of day or seasons. The research contributions presented in the thesis advance visual place recognition techniques, making them more useful for deployment in a wide range of robotic and autonomous vehicle scenarios.
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Bethel, Cindy L. "Robots Without Faces: Non-Verbal Social Human-Robot Interaction." [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0003097.
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Prakash, Akanksha. "Understanding younger and older adults' perceptions of humanoid robots: effects of facial appearance and task." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/49024.
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Although humanoid robots are being designed to assist people in various tasks, there remain gaps in our understanding of the perceptions that humanoid faces evoke in the user. Understanding user perceptions would help design robots that are better suited for the target user group. Younger and older adults’ preferences for robot appearance were assessed out of three levels of human-likeness. In general, people perceived a mixed human-robot appearance less favorably compared to highly human and highly robotic appearances. Additionally the nature of task also influenced people’s overall perceptions of robots. Robots were most positively evaluated for assistance with chores and less positively for personal care and decision-making. Moreover, task and robot humanness had an interactive effect on people’s likability, trust, and perceived usefulness toward robots.
Age-related differences in preferences of robot humanness were also observed. Older adults showed a higher inclination toward human-looking appearance of robots whereas younger adults’ preferences were more distributed across the levels of humanness. An appearance with mixed human-robot features was more likely to be rejected by older adults than by younger adults, and the difference was most striking for a decision-making task. Besides the humanness of the robot face, perceptions of robot appearances were also influenced by factors such as robot gender, specific facial features/aesthetics, expressiveness, perceived personality, and perceived capability. Future studies should measure the relative weight of these different factors in the formation of perceptions, both at a global level and at a task-specific level.
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Paul, Rohan. "Long term appearance-based mapping with vision and laser." Thesis, University of Oxford, 2012. http://ora.ox.ac.uk/objects/uuid:8d59bf8c-bec8-4782-b100-aa80d1136802.
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This thesis is about appearance-based topological mapping for mobile robots using vision and laser. Our goal is life-long continual operation in outdoor unstruc- tured workspaces. We present a new probabilistic framework for appearance-based mapping and navigation incorporating spatial and visual appearance. Locations are encoded prob- abilistically as random graphs possessing latent distributions over visual features and pair-wise euclidean distances generating observations modeled as 3D constellations of features observed via noisy range and visual detectors. Multi-modal distributions over inter-feature distances are learnt using non-parametric kernel density estima- tion. Inference is accelerated by executing a Delaunay tessellation of the observed graph with minimal loss in performance, scaling log-linearly with scene complexity. Next, we demonstrate how a robot can, through introspection and then targeted data retrieval, improve its own place recognition performance. We introduce the idea of a dynamic sampling set, the onboard workspace representation, that adapts with increasing visual experience of continually operating robot. Based on a topic based probabilistic model of images, we use a measure of perplexity to evaluate how well a working set of background images explains the robot’s online view of the world. O/ine, the robot then searches an external resource to seek additional background images that bolster its ability to localize in its environment when used next. Finally, we present an online and incremental approach allowing an exploring robot to generate apt and compact summaries of its life experience using canon- ical images that capture the essence of the robot’s visual experience-illustrating both what was ordinary and what was extraordinary. Leveraging probabilistic topic models and an incremental graph clustering technique we present an algorithm that scales well with time and variation of experience, generating a summary that evolves incrementally with the novelty of data.
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Fanelli, Gabriele. "Facial Features Tracking using Active Appearance Models." Thesis, Linköping University, Department of Electrical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7658.
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This thesis aims at building a system capable of automatically extracting and parameterizing the position of a face and its features in images acquired from a low-end monocular camera. Such a challenging task is justified by the importance and variety of its possible applications, ranging from face and expression recognition to animation of virtual characters using video depicting real actors. The implementation includes the construction of Active Appearance Models of the human face from training images. The existing face model Candide-3 is used as a starting point, making the translation of the tracking parameters to standard MPEG-4 Facial Animation Parameters easy.
The Inverse Compositional Algorithm is employed to adapt the models to new images, working on a subspace where the appearance is "projected out" and thus focusing only on shape.
The algorithm is tested on a generic model, aiming at tracking different people’s faces, and on a specific model, considering one person only. In the former case, the need for improvements in the robustness of the system is highlighted. By contrast, the latter case gives good results regarding both quality and speed, with real time performance being a feasible goal for future developments.
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Cummins, Mark. "Probabilistic localization and mapping in appearance space." Thesis, University of Oxford, 2009. http://ora.ox.ac.uk/objects/uuid:a34370f2-a2a9-40b5-9a2d-1c8c616ff07a.
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This thesis is concerned with the problem of place recognition for mobile robots. How can a robot determine its location from an image or sequence of images, without any prior knowledge of its position, even in a world where many places look identical? We outline a new probabilistic approach to the problem, which we call Fast Appearance Based Mapping or FAB-MAP. Our map of the environment consists of a set of discrete locations, each with an associated appearance model. For every observation collected by the robot, we compute a probability distribution over the map, and either create a new location or update our belief about the appearance of an existing location. The technique can be seen as a new type of SLAM algorithm, where the appearance of locations (rather than their position) is subject to estimation. Unlike existing SLAM systems, our appearance based technique does not rely on keeping track of the robot in any metric coordinate system. Thus it is applicable even when informative observations are available only intermittently. Solutions to the loop closure detection problem, the kidnapped robot problem and the multi-session mapping problem arise as special cases of our general approach. Abstract Our probabilistic model introduces several technical advances. The model incorporates correlations between visual features in a novel way, which is shown to improve system performance. Additionally, we explicitly compute an approximation to the partition function in our Bayesian formulation, which provides a natural probabilistic measure of when a new observation should be assigned to a location not already present in the map. The technique is applicable even in visually repetitive environments where many places look the same. Abstract Finally, we define two distinct approximate inference procedures for the model. The first of these is based on concentration inequalities and has general applicability beyond the problem considered in this thesis. The second approach, built on inverted index techniques, is tailored to our specific problem of place recognition, but achieves extreme efficiency, allowing us to apply FAB-MAP to navigation problems on the largest scale. The thesis concludes with a visual SLAM experiment on a trajectory 1,000 km long. The system successfully detects loop closures with close to 100% precision and requires average inference time of only 25 ms by the end of the trajectory.
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Stewart, Alexander D. "Localisation using the appearance of prior structure." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:4ee889ac-e8e3-4000-ae23-a9d7f84fcd65.
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Accurate and robust localisation is a fundamental aspect of any autonomous mobile robot. However, if these are to become widespread, it must also be available at low-cost. In this thesis, we develop a new approach to localisation using monocular cameras by leveraging a coloured 3D pointcloud prior of the environment, captured previously by a survey vehicle. We make no assumptions about the external conditions during the robot's traversal relative to those experienced by the survey vehicle, nor do we make any assumptions about their relative sensor configurations. Our method uses no extracted image features. Instead, it explicitly optimises for the pose which harmonises the information, in a Shannon sense, about the appearance of the scene from the captured images conditioned on the pose, with that of the prior. We use as our objective the Normalised Information Distance (NID), a true metric for information, and demonstrate as a consequence the robustness of our localisation formulation to illumination changes, occlusions and colourspace transformations. We present how, by construction of the joint distribution of the appearance of the scene from the prior and the live imagery, the gradients of the NID can be computed and how these can be used to efficiently solve our formulation using Quasi-Newton methods. In order to reliably identify any localisation failures, we present a new classifier using the local shape of the NID about the candidate pose and demonstrate the performance gains of the complete system from its use. Finally, we detail the development of a real-time capable implementation of our approach using commodity GPUs and demonstrate that it outperforms a high-grade, commercial GPS-aided INS on 57km of driving in central Oxford, over a range of different conditions, times of day and year.
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Knurek, Jeffrey. "Exploring the Implementation of Complex Appearances on Small Robots." Thesis, Blekinge Tekniska Högskola, Avdelningen för för interaktion och systemdesign, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4717.
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The purpose of this project is the exploration of how autonomous robots could develop a language to communicate visual patterns. The idea is that each robot should be able to change their visual appearance depending on what its neighbors are trying to communicate. Thus, the robots should `talk' about their patterns, trying to influence each other. For this project we used the e-Puck robot, a small mobile robot developed by EPFL (Ecole Polytechnique F\'{e}d\'{e}rale de Lausanne) in conjunction with the see-Puck display. The display, developed by FAL (Future Applications Lab), consists of a matrix of 148 LEDs in a circular shape. During this project we looked into several methods of achieving communication though the sensors and actuators of the e-Puck robot. An additional area which was explored was the process of user interaction with the robots.
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Walters, Michael L. "The design space for robot appearance and behaviour for social robot companions." Thesis, University of Hertfordshire, 2008. http://hdl.handle.net/2299/1806.
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To facilitate necessary task-based interactions and to avoid annoying or upsetting people a domestic robot will have to exhibit appropriate non-verbal social behaviour. Most current robots have the ability to sense and control for the distance of people and objects in their vicinity. An understanding of human robot proxemic and associated non-verbal social behaviour is crucial for humans to accept robots as domestic or servants. Therefore, this thesis addressed the following hypothesis: Attributes of robot appearance, behaviour, task context and situation will affect the distances that people will find comfortable between themselves and a robot. Initial exploratory Human-Robot Interaction (HRI) experiments replicated human-human studies into comfortable approach distances with a mechanoid robot in place of one of the human interactors. It was found that most human participants respected the robot's interpersonal space and there were systematic differences for participants' comfortable approach distances to robots with different voice styles. It was proposed that greater initial comfortable approach distances to the robot were due to perceived inconsistencies between the robots overall appearance and voice style. To investigate these issues further it was necessary to develop HRI experimental set-ups, a novel Video-based HRI (VHRI) trial methodology, trial data collection methods and analytical methodologies. An exploratory VHRI trial then investigated human perceptions and preferences for robot appearance and non-verbal social behaviour. The methodological approach highlighted the holistic and embodied nature of robot appearance and behaviour. Findings indicated that people tend to rate a particular behaviour less favourably when the behaviour is not consistent with the robot’s appearance. A live HRI experiment finally confirmed and extended from these previous findings that there were multiple factors which significantly affected participants preferences for robot to human approach distances. There was a significant general tendency for participants to prefer either a tall humanoid robot or a short mechanoid robot and it was suggested that this may be due to participants internal or demographic factors. Participants' preferences for robot height and appearance were both found to have significant effects on their preferences for live robot to Human comfortable approach distances, irrespective of the robot type they actually encountered. The thesis confirms for mechanoid or humanoid robots, results that have previously been found in the domain of human-computer interaction (cf. Reeves & Nass (1996)), that people seem to automatically treat interactive artefacts socially. An original empirical human-robot proxemic framework is proposed in which the experimental findings from the study can be unified in the wider context of human-robot proxemics. This is seen as a necessary first step towards the desired end goal of creating and implementing a working robot proxemic system which can allow the robot to: a) exhibit socially acceptable social spatial behaviour when interacting with humans, b) interpret and gain additional valuable insight into a range of HRI situations from the relative proxemic behaviour of humans in the immediate area. Future work concludes the thesis.
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Bessinger, Zachary. "Modeling and Mapping Location-Dependent Human Appearance." UKnowledge, 2018. https://uknowledge.uky.edu/cs_etds/75.
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Human appearance is highly variable and depends on individual preferences, such as fashion, facial expression, and makeup. These preferences depend on many factors including a person's sense of style, what they are doing, and the weather. These factors, in turn, are dependent upon geographic location and time. In our work, we build computational models to learn the relationship between human appearance, geographic location, and time. The primary contributions are a framework for collecting and processing geotagged imagery of people, a large dataset collected by our framework, and several generative and discriminative models that use our dataset to learn the relationship between human appearance, location, and time. Additionally, we build interactive maps that allow for inspection and demonstration of what our models have learned.
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JIANG, JINWEI. "Collaborative Tracking of Image Features Based on Projective Invariance." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1345562896.
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Bacca, Cortés Eval Bladimir. "Appearance-based mapping and localization using feature stability histograms for mobile robot navigation." Doctoral thesis, Universitat de Girona, 2012. http://hdl.handle.net/10803/83589.
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This work proposes an appearance-based SLAM method whose main contribution is the Feature Stability Histogram (FSH). The FSH is built using a voting schema, if the feature is re-observed, it will be promoted; otherwise it progressively decreases its corresponding FSH value. The FSH is based on the human memory model to deal with changing environments and long-term SLAM. This model introduces concepts of Short-Term memory (STM), which retains information long enough to use it, and Long-Term memory (LTM), which retains information for longer periods of time. If the entries in the STM are rehearsed, they become part of the LTM (i.e. they become more stable). However, this work proposes a different memory model, allowing to any input be part of the STM or LTM considering the input strength. The most stable features are only used for SLAM. This innovative feature management approach is able to cope with changing environments, and long-term SLAM.Este trabajo propone un método de SLAM basado en apariencia cuya principal contribución es el Histograma de Estabilidad de Características (FSH). El FSH es construido por votación, si una característica es re-observada, ésta será promovida; de lo contrario su valor FSH progresivamente es reducido. El FSH es basado en el modelo de memoria humana para ocuparse de ambientes cambiantes y SLAM a largo término. Este modelo introduce conceptos como memoria a corto plazo (STM) y largo plazo (LTM), las cuales retienen información por cortos y largos periodos de tiempo. Si una entrada a la STM es reforzada, ésta hará parte de la LTM (i.e. es más estable). Sin embargo, este trabajo propone un modelo de memoria diferente, permitiendo a cualquier entrada ser parte de la STM o LTM considerando su intensidad. Las características más estables son solamente usadas en SLAM. Esta innovadora estrategia de manejo de características es capaz de hacer frente a ambientes cambiantes y SLAM de largo término.
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Andreasson, Henrik. "Local visual feature based localisation and mapping by mobile robots." Doctoral thesis, Örebro : Örebro University, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-2444.
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Lowry, Stephanie Margaret. "Visual place recognition for persistent robot navigation in changing environments." Thesis, Queensland University of Technology, 2014. https://eprints.qut.edu.au/79404/1/Stephanie_Lowry_Thesis.pdf.
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This thesis demonstrates that robots can learn about how the world changes, and can use this information to recognise where they are, even when the appearance of the environment has changed a great deal. The ability to localise in highly dynamic environments using vision only is a key tool for achieving long-term, autonomous navigation in unstructured outdoor environments. The proposed learning algorithms are designed to be unsupervised, and can be generated by the robot online in response to its observations of the world, without requiring information from a human operator or other external source.
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Sünderhauf, Niko. "Robust optimization for simultaneous localization and mapping." Thesis, Technischen Universitat Chemnitz, 2012. https://eprints.qut.edu.au/109667/1/109667.pdf.
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SLAM (Simultaneous Localization And Mapping) has been a very active and almost ubiquitous problem in the field of mobile and autonomous robotics for over two decades. For many years, filter-based methods have dominated the SLAM literature, but a change of paradigms could be observed recently. Current state of the art solutions of the SLAM problem are based on efficient sparse least squares optimization techniques. However, it is commonly known that least squares methods are by default not robust against outliers . In SLAM, such outliers arise mostly from data association errors like false positive loop closures. Since the optimizers in current SLAM systems are not robust against outliers, they have to rely heavily on certain preprocessing steps to prevent or reject all data association errors. Especially false positive loop closures will lead to catastrophically wrong solutions with current solvers. The problem is commonly accepted in the literature, but no concise solution has been proposed so far.
The main focus of this work is to develop a novel formulation of the optimization-based SLAM problem that is robust against such outliers. The developed approach allows the back-end part of the SLAM system to change parts of the topological structure of the problem’s factor graph representation during the optimization process. The back-end can thereby discard individual constraints and converge towards correct solutions even in the presence of many false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. The approach is evaluated on both large scale synthetic and real-world datasets.
This work furthermore shows that the developed approach is versatile and can be applied beyond SLAM, in other domains where least squares optimization problems are solved and outliers have to be expected. This is successfully demonstrated in the domain of GPS-based vehicle localization in urban areas where multipath satellite observations often impede high-precision position estimates.
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Sünderhauf, Niko. "Robust Optimization for Simultaneous Localization and Mapping." Doctoral thesis, Universitätsbibliothek Chemnitz, 2012. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-86443.
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SLAM (Simultaneous Localization And Mapping) has been a very active and almost ubiquitous problem in the field of mobile and autonomous robotics for over two decades. For many years, filter-based methods have dominated the SLAM literature, but a change of paradigms could be observed recently.
Current state of the art solutions of the SLAM problem are based on efficient sparse least squares optimization techniques. However, it is commonly known that least squares methods are by default not robust against outliers. In SLAM, such outliers arise mostly from data association errors like false positive loop closures. Since the optimizers in current SLAM systems are not robust against outliers, they have to rely heavily on certain preprocessing steps to prevent or reject all data association errors. Especially false positive loop closures will lead to catastrophically wrong solutions with current solvers. The problem is commonly accepted in the literature, but no concise solution has been proposed so far.
The main focus of this work is to develop a novel formulation of the optimization-based SLAM problem that is robust against such outliers. The developed approach allows the back-end part of the SLAM system to change parts of the topological structure of the problem\'s factor graph representation during the optimization process. The back-end can thereby discard individual constraints and converge towards correct solutions even in the presence of many false positive loop closures. This largely increases the overall robustness of the SLAM system and closes a gap between the sensor-driven front-end and the back-end optimizers. The approach is evaluated on both large scale synthetic and real-world datasets.
This work furthermore shows that the developed approach is versatile and can be applied beyond SLAM, in other domains where least squares optimization problems are solved and outliers have to be expected. This is successfully demonstrated in the domain of GPS-based vehicle localization in urban areas where multipath satellite observations often impede high-precision position estimates.
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Neubert, Peer. "Superpixels and their Application for Visual Place Recognition in Changing Environments." Doctoral thesis, Universitätsbibliothek Chemnitz, 2015. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-190241.
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Superpixels are the results of an image oversegmentation. They are an established intermediate level image representation and used for various applications including object detection, 3d reconstruction and semantic segmentation. While there are various approaches to create such segmentations, there is a lack of knowledge about their properties. In particular, there are contradicting results published in the literature. This thesis identifies segmentation quality, stability, compactness and runtime to be important properties of superpixel segmentation algorithms. While for some of these properties there are established evaluation methodologies available, this is not the case for segmentation stability and compactness. Therefore, this thesis presents two novel metrics for their evaluation based on ground truth optical flow. These two metrics are used together with other novel and existing measures to create a standardized benchmark for superpixel algorithms. This benchmark is used for extensive comparison of available algorithms. The evaluation results motivate two novel segmentation algorithms that better balance trade-offs of existing algorithms: The proposed Preemptive SLIC algorithm incorporates a local preemption criterion in the established SLIC algorithm and saves about 80 % of the runtime. The proposed Compact Watershed algorithm combines Seeded Watershed segmentation with compactness constraints to create regularly shaped, compact superpixels at the even higher speed of the plain watershed transformation.
Operating autonomous systems over the course of days, weeks or months, based on visual navigation, requires repeated recognition of places despite severe appearance changes as they are for example induced by illumination changes, day-night cycles, changing weather or seasons - a severe problem for existing methods. Therefore, the second part of this thesis presents two novel approaches that incorporate superpixel segmentations in place recognition in changing environments. The first novel approach is the learning of systematic appearance changes. Instead of matching images between, for example, summer and winter directly, an additional prediction step is proposed. Based on superpixel vocabularies, a predicted image is generated that shows, how the summer scene could look like in winter or vice versa. The presented results show that, if certain assumptions on the appearance changes and the available training data are met, existing holistic place recognition approaches can benefit from this additional prediction step. Holistic approaches to place recognition are known to fail in presence of viewpoint changes. Therefore, this thesis presents a new place recognition system based on local landmarks and Star-Hough. Star-Hough is a novel approach to incorporate the spatial arrangement of local image features in the computation of image similarities. It is based on star graph models and Hough voting and particularly suited for local features with low spatial precision and high outlier rates as they are expected in the presence of appearance changes. The novel landmarks are a combination of local region detectors and descriptors based on convolutional neural networks. This thesis presents and evaluates several new approaches to incorporate superpixel segmentations in local region detection. While the proposed system can be used with different types of local regions, in particular the combination with regions obtained from the novel multiscale superpixel grid shows to perform superior to the state of the art methods - a promising basis for practical applications.
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Somanader, Mark. "Children use appearance and origin of motion to categorize robots." Diss., 2008. http://etd.library.vanderbilt.edu/ETD-db/available/etd-09042008-143734/.
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McManus, Colin. "Visual Teach and Repeat Using Appearance-based Lidar - A Method For Planetary Exploration." Thesis, 2011. http://hdl.handle.net/1807/31339.
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Future missions to Mars will place heavy emphasis on scientific sample and return operations, which will require a rover to revisit sites of interest. Visual Teach and Repeat (VT&R) has proven to be an effective method to enable autonomous repeating of any previously driven route without a global positioning system. However, one of the major challenges in recognizing previously visited locations is lighting change, as this can drastically change the appearance of the scene. In an effort to achieve lighting invariance, this thesis details the design of a VT&R system that uses a laser scanner as the primary sensor. The key novelty is to apply appearance-based vision techniques traditionally used with camera systems to laser intensity images for motion estimation. Field tests were conducted in an outdoor environment over an entire diurnal cycle, covering more than 11km with an autonomy rate of 99.7% by distance.
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Mays, Kate Keener. "Humanizing robots? The influence of appearance and status on social perceptions of robots." Thesis, 2021. https://hdl.handle.net/2144/41877.
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Social robots are a lesser known technology with uncertain but seemingly very powerful potential, which for decades has been portrayed in cultural artifacts as threats to human primacy. Research on people’s relationships to non-robotic technology, however, indicates that people will treat robots socially and assimilate them into their lives in ways that may disrupt existing norms but still fulfill a fundamental human need. Through the theoretical lenses of media equation and apparatgiest, this dissertation examines facets of robot humanization, defined as how people think of robots as social and human-like entities through perceptions of liking, human-likeness, and rights’ entitlement. In a 2 (gender) x 2 (physical humanness) x 3 (status) between-subjects online experiment, this dissertation explores the influence of fixed technological traits (the robot’s gender, physical humanness, and described status) and participants’ individual differences on humanization perceptions. Findings show that the robots’ features mattered less than participants’ individual traits, which explained the most variance in humanizing perceptions of social robots. Of those, participants’ prior robot exposure (both in real life and mediated) and efficacy traits were the strongest predictors of robot liking, perceived human-likeness, and perceptions of rights entitlement. Specifically, those with more real-life exposure and who perceived themselves as more technologically competent were more likely to humanize robots, while those with higher internal loci of control and negative mediated views of robots were less inclined to humanize robots. Theoretically, this study’s findings suggest that technological affordances matter less than the ontological understanding that social robots as a category may have in people’s humanizing perceptions. Looking forward, these findings indicate that there is an opportunity in the design of social robots to set precedents now that are prosocial and reflective of the world people strive for and want to inhabit in the future.