Robot Localization in a Pipe Network using a Particle Filter with Error Detection and Recovery in a Hybrid Metric-Topological Space

Abstract

Estimation of a robot's pose in the constrained environment of a buried pipe network is difficult due to uncertainty in motion, limited and unreliable sensing, and limited computational capability on board the robot. An efficient localization algorithm is described here, along with two novel improvements: the detection of mislocalization using the Kullback-Leibler divergence between the distribution of recent particle filter weights and a distribution learned from data for good algorithm performance, overcoming the problem of low information in each individual set of particle filter weights; and the capability for relocalization, using multi-hypothesis estimation to overcome the problems of limited information in sensing. The algorithm uses the low-dimensional metric-topological space of the pipe network to give efficient performance and to facilitate relocalization. Experimental results show that the localization algorithm is effective and that the presented improvements reduce the chance of unrecoverable mislocalization, therefore improving robustness to error in measurements made by the robot.