  For SLAM to be safely deployed in unstructured real world environments, it
must possess several key properties that are not encompassed by conventional
benchmarks. In this paper we show that SLAM commutativity, that is, consistency
in trajectory estimates on forward and reverse traverses of the same route, is
a significant issue for the state of the art. Current pipelines show a
significant bias between forward and reverse directions of travel, that is in
addition inconsistent regarding which direction of travel exhibits better
performance. In this paper we propose several contributions to feature-based
SLAM pipelines that remedies the motion bias problem. In a comprehensive
evaluation across four datasets, we show that our contributions implemented in
ORB-SLAM2 substantially reduce the bias between forward and backward motion and
additionally improve the aggregated trajectory error. Removing the SLAM motion
bias has significant relevance for the wide range of robotics and computer
vision applications where performance consistency is important.
