Spatial Cognition 2004: Spatial Cognition IV. Reasoning, Action, Interaction pp 434-454 | Cite as
Using 2D and 3D Landmarks to Solve the Correspondence Problem in Cognitive Robot Mapping
Conference paper
Abstract
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.
Keywords
Scale Invariant Feature Transform Camera View Local Space Correspondence Problem Scale Invariant Feature Transform Feature
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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