幾何地図上での観測物体の有無を考慮した自己位置推定

抄録

This paper presents a localization approach that simultaneously estimates a robot's pose and class of sensor observations, where “class” categorizes the sensor observations as those obtained from known and unknown objects on a given geometric map. The proposed approach is implemented using Rao-Blackwellized particle filtering algorithm. The robot's pose can be robustly estimated utilizing sensor observations obtained from the only known objects by the simultaneous estimation. The proposed approach is efficient in terms of computational complexity because its complexity is same as that of the likelihood field model. Performance of the proposed approach was shown through experiments using a 2D LiDAR simulator.