Abstract
This paper describes a GA approach for solving the SLAM problem. We treat the local scan from laser sensor as an image pattern. Two subsequent scans were matched to find the robot's ego motion. This motion is used as updates for robot's global position estimation in the map. A virtual scan is obtained from the map in the robot's current position. This virtual scan is then matched with the current laser scan to update robot's location in the map. The innovation in this paper is the matching algorithm to determine the relative location of two patterns: (1) two successive scans and (2) current scan with virtual scans taken from the map. The matching algorithm is to find the optimal location of one pattern to the other, namely (dx, dy, de) (two for translation and one for rotation). This is reduced to a three-dimensional search problem. The GA we developed can achieve high accuracy with fast computational time. Experiments are performed under simulated and real data. The performance obtained outperforms ICP.
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