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The evidence grid representation was formulated at the CMU Mobile Robot Laboratory in 1983 to turn wide angle range measurements from cheap mobile robot-mounted sonar sensors into detailed spatial maps. It accumulates diffuse evidence about the occupancy of a grid of small volumes of nearby space from individual sensor readings into increasingly confident and detailed maps of a robot's surroundings. It worked surprisingly well in first implementation for sonar navigation in cluttered rooms. In the past decade its use has been extended to range measurements from stereoscopic vision and other sensors, sonar in very difficult specular environments, and other contexts. The most dramatic extension yet, from 2D grid maps with thousands of cells to 3D grids with millions, is underway.
This talk presents the mathematical and probabilistic framework we now use for evidence grids. It gives the history of the grid representation, and its relation to other spatial modeling approaches. It discusses earlier formulations and their limitations, and mentions several extensions. Finally, a list of open issues and research topics is presented.
Martin C. Martin is a third year PhD student at the Robotics Institute, CMU. He received his undergraduate degree in Cognitive Science from the University of Toronto in 1993. His research is in the field of autonomous mobile robots. His interests are in using some form of genetic algorithm to develop a non-modular (i.e. highly interdependent) navigation algorithm for unaugmented indoor, office environments.