ニューラルネットワークを用いて複数の推定手段を融合する移動ロボットの自己位置推定法

抄録

Many localization methods have been proposed for an autonomous mobile robot and many studies focus on enhancement of capability of each means. Monte Carlo localization such as particle filter has good robustness and widely used for mobile robots but lacks precise estimation in a certain environment. ICP(Iterative Closest Points) matching has good performance about estimation precision when there are many good features such as many straight walls but lacks robustness if there are few distinguish feature points around the robot. In this paper we propose a fusion strategy using neural network which can be applied to many fields in a building such as an indoor public space. Simulation results for 3 characteristic environments show effectiveness of the proposed strategy.