Preventing and Correcting Mistakes in Lifelong Mapping

A Graph SLAM system is only as good as the edges in its pose graph. Critical mistakes in the generation of these edges can instantly render a map inconsistent, misleading, and ultimately unusable. For a lifelong mapping system, where the map is updated continuously, avoiding these errors altogether is infeasible. Instead, we propose a system for detection of and recovery from severe errors in edge generation. Our system remedies both edges created by view observations and edges created by an odometry motion model. For observation edges, we pair a novel method for monitoring ambiguous views with an intelligent graph-merging algorithm capable of rejecting a relocalization in progress. For motion edges, we propose a qualitative geometric approach for detecting structural aberrations characteristic of odometry failures. We conclude with an analysis of our results based on an empirical study of thousands of robot runs.