A Sequential Decision-theoretic Method for Detecting Mobile Robots Localization Failures

Many methods in mobile robotics usually utilize current sensor measurement to evaluate the localization performance of robots, for example in scan matching and particle filter methods. This immediately detecting methodology tend to cause a problem that a well-localization robot obtains a poor sensor measurement, the robot may mistake momentary observation noise for a localization failure. In this paper, we propose a new robot localization fault detection method for resolving this problem. We model robot localization fault detection as a sequential decision-making problem, where the decision of detecting a localization failure is based on a long-term sensor measurements. We employ two parameters of false-positive and false-negative observation error probabilities, which can eliminate the influence of noisy observations. Further, the proposed method derives Bayesian update equations for the integration of a long-term observations and presents an analytic formula representing the belief function of the reliability of localization results. Experimental studies validate the effectiveness of the proposed method.