Multiple target tracking with navigation uncertainty

Abstract

The goal of concurrent mapping and localization (CML) is for a mobile robot to build a map of an unknown environment while simultaneously using that map to navigate. CML can be considered as a problem of multiple target tracking (MTT) in the presence of navigation uncertainty. Although data association errors can have a catastrophic effect on CML performance, previous approaches to CML, such as stochastic mapping (SM), have either ignored the data association problem, matched features by hand, or used a nearest-neighbor approach. We have developed integrated mapping and navigation (IMAN), a multiple hypothesis approach to CML that generalizes SM to incorporate data association uncertainty and expands multiple hypothesis tracking (MHT) to accommodate navigation error. The paper summarizes IMAN and illustrates its performance for a simulation of an autonomous underwater vehicle (AUV) navigating with a forward-looking sonar.