Formal Performance Guarantees for Behavior-Based Localization Missions

Abstract

Localization and mapping algorithms can allow a robot to navigate well in an unknown environment. However, whether such algorithms enhance any specific robot mission is currently a matter for empirical validation. In this paper we apply our MissionLab/VIPARS mission design and verification approach to an autonomous robot mission that uses probabilistic localization software. Two approaches to modeling probabilistic localization for verification are presented: a high-level approach, and a sample-based approach which allows run-time code to be embedded in verification. Verification and experimental validation results are presented for two waypoint missions using each method, demonstrating the accuracy of verification, and both are compared with verification of an odometry-only mission, to show the mission-specific benefit of localization.