Abstract
Perception-driven hierarchical simultaneous localization and mapping approach is proposed based on a distributed particle of percolator model adapted to search and rescue post-disaster environment....
Highlights
Simultaneous localization and mapping (SLAM) is vital process for mobile robots conducting search and rescue (SAR) tasks while extracting external features from a series of images
Hierarchical SLAM is conducted by the two types of mobile robots in artificial cluttered SAR environments and real unstructured collapsed scenarios
The exploration area coverage rates are compared between the hierarchical iterated extended Kalman filter (IEKF) SAR SLAM and the ordinary traditional SLAM, and the experimental effects demonstrate that the proposed methods have higher area coverage rates comparing to the conventional ones
Summary
Simultaneous localization and mapping (SLAM) is vital process for mobile robots conducting search and rescue (SAR) tasks while extracting external features from a series of images. Local sub-maps are related to a mobile robot’s series of positions and, over these fingerprints, a global topological map is formed.[8] In a graph-based hierarchical procedure, the fusion of GPS data and dead reckoning IMUs is not highly reliable due to the robot’s periodic trajectory; the complexity of the covariance computation in using an EKF algorithm constraints the process to low-level SLAM.
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