Autonomous navigation in unknown environments using robust SLAM

Autonomous navigation in unknown environment is a big challenge. In this paper, we combine the SLAM (simultaneous localization and mapping) with the path planning method. We first modify the classical SLAM with sliding mode technique, such that it is robust in the unknown environment. Then we analyze the algorithm using the “known space” and “free space” conditions, and propose the polar histogram path planning based on these conditions. We use Monte Carlo method to evaluate the performance of our algorithms. Simulation results show that our autonomous navigation algorithms are better than the others in unknown environment.

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