A Simultaneous Localization and Mapping Approach by Combining Particle Filter and Dot-line Congruence
基于粒子滤波和点线相合的未知环境地图构建方法

To reduce the memory and computation load of traditional simultaneous localization and mapping methods based on particle filter, this paper presents a new approach by introducing the incremental mapping algorithm based on dot-line congruence into particle filter, in which the unknown environment is described by segment-features map. In the approach, each particle carries an individual segment-features map of the environment. Both the motion and the observation information are considered in the importance function by using the dot-line congruence method to estimate the pose of a robot. The weight of the particle is updated according to the congruence between current measurement and segment features in previously built map. The wrong particles resulted from mis-matching or error accumulation are filtered with selective resampling. Analysis shows that the complexity of our method is low. Experimental results with real data are presented, which demonstrate the approach is effective and robust for indoor environment mapping. Both of the memory and particle numbers are quite smaller than those of the existing mapping methods using particle filter.