基于信息势能的鲁棒估计器及其在同时定位与地图构建问题中的应用

摘要

提出了一种基于信息势能鲁棒估计器来解决机器人室内的同时定位与地图构建（SLAM）问题．结构化的室内环境可以用线段近似表示．然而动态环境中，测距传感器测得的数据通常湮没在大量的噪声信号中．本文采用“分割与合并”（split—and．merge）方法进行线段的分类，根据信息势能的性能指标衡量每个采样数据对该线段的信息贡献量．按照信息优化理论设计估计器，选择信息量贡献大的样本点作为信息内点提取线段参数，构建局部地图．采用粒子滤波器进行地图及机器人路径的更新．采用递推的方法估计信息势能，降低了对样本点的信息量贡献%We present a novel robust estimator based on information potential optimization techniques and apply it to simultaneous localization and mapping on segment-based maps. Structured indoor environment can be efficiently described with Segment-based maps. Usually, in dynamic environment, sample data collected by range-finders suffer from noises and disturbances. Sample data are divided into clusters with split-and-merge. Inliers of the segment are selected according to the information contribution which is measured by information potential. After the local map is built, particle filters are adopted to update robot poses and maps. The recursive information potential reduces computations of information contribution of each sample. Simulations and experimental results validate the strong robustness of the proposed estimator, and the accuracy and efficiency of the proposed strategy based on the robust estimator.