Towards a computational theory of cognitive maps
Artificial Intelligence
Learning metric-topological maps for indoor mobile robot navigation
Artificial Intelligence
The spatial semantic hierarchy
Artificial Intelligence
Computer Vision: Three-Dimensional Data from Images
Computer Vision: Three-Dimensional Data from Images
Bootstrap learning for place recognition
Eighteenth national conference on Artificial intelligence
FastSLAM: a factored solution to the simultaneous localization and mapping problem
Eighteenth national conference on Artificial intelligence
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Computing a representation of the local environment
Artificial Intelligence
Initial experiments with a mobile robot on cognitive mapping
PCAR '06 Proceedings of the 2006 international symposium on Practical cognitive agents and robots
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We present an approach which uses 2D and 3D landmarks for solving the correspondence problem in Simultaneous Localisation and Mapping (SLAM) in cognitive robot mapping. The nodes in the topological map are a representation for each local space the robot visits. The 2D approach is feature based – a neural network algorithm is used to learn a landmark signature from a set of features extracted from each local space representation. Newly encountered local spaces are classified by the neural network as to how well they match the signatures of the nodes in the topological network. The 3D landmarks are computed from camera views of the local space. Using multiple 2D views, identified landmarks are projected, with their correct location and orientation into 3D world space by scene reconstruction. As the robot moves around the local space, extracted landmarks are integrated into the ASR's scene representation which comprises the 3D landmarks. The landmarks for an ASR scene are compared against the landmark scenes for previously constructed ASRs to determine when the robot is revisiting a place it has been to before.