IEEE Transactions on Pattern Analysis and Machine Intelligence
Some location problems for robot navigation using a single camera
Computer Vision, Graphics, and Image Processing
Qualitative navigation for mobile robots
Artificial Intelligence
Gross motion planning—a survey
ACM Computing Surveys (CSUR)
Active vision
Modeling a dynamic environment using a Bayesian multiple hypothesis approach
Artificial Intelligence
Vision-based motion planning and exploration algorithms for mobile robots
WAFR Proceedings of the workshop on Algorithmic foundations of robotics
Robot Motion Planning
Modeling Structured Environments Using Robot Vision
ACCV '95 Invited Session Papers from the Second Asian Conference on Computer Vision: Recent Developments in Computer Vision
Visual navigation using a single camera
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
Toward selecting and recognizing natural landmarks
IROS '95 Proceedings of the International Conference on Intelligent Robots and Systems-Volume 1 - Volume 1
Selecting One Among the Many: A Simple Network Implementing Shifts in Selective Visual Attention
Selecting One Among the Many: A Simple Network Implementing Shifts in Selective Visual Attention
Visual navigation
Learning to select useful landmarks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Experiences from the use of a robotic avatar in a museum setting
Proceedings of the 2001 conference on Virtual reality, archeology, and cultural heritage
Definition and Extraction of Visual Landmarks for Indoor Robot Navigation
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Environment topological structure recognition for robot navigation
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Natural landmark based navigation
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
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In this work, robot navigation is approached using visual landmarks.Landmarks are not preselected or otherwise defined a priori;they are extracted automatically during a learning phase. To facilitate this, a saliency map is constructed on the basis ofwhich potential landmarks are highlighted. This is used inconjunction with a model-driven segregation of the workspace tofurther delineate search areas for landmarks in the environment. For the sake of robustness, no semantic information is attached tothe landmarks; they are stored as raw patterns, along with informationreadily available from the workspace segregation. This subsequentlyfacilitates their accurate recognition during a navigation session,when similar steps are employed to locate landmarks, as in thelearning phase. The stored information is used to transform apreviously learned landmark pattern, according to the current positionof the observer, thus achieving accurate landmark recognition. Results obtained using this approach demonstrate its validity andapplicability in indoor workspaces.