Abstract
3D map building and path planning serve as two essential tasks for mobile robot to work within a complex outdoor environment. An elevation map built from 3D laser points is utilised to extract terrain feature while ground surface character is acquired from visual information by matching characteristics vectors. By projecting the units of the elevation map into the image, the fusion of terrain feature and ground surface character is achieved using statistical method. Then the units which fuse multi-sensor information are evaluated and given traversable weights to extract constraints for autonomous path planning in outdoor scene. After clustering the units into several regions, a hierarchical path planning strategy uses A* and Probabilistic Roadmap Method (PRM) to plan region paths and unit paths, respectively. The PRM is improved by choosing the units with the largest traversable weights in the regions to ensure a uniform distribution instead of random distribution. Experiment results show the validity and practicability of the proposed approaches.
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