Online place recognition calibration for out-of-the-box SLAM

Localization is a critical capability for many autonomous mobile robots. Implicit in all localization and place recognition techniques is the ability to decide whether a place is novel or familiar; the idea of “has this place been visited before?”. To make this decision accurately, localization systems typically require prior calibration or training on datasets from the specific type of operating environment; a requirement that is not practical in many environments or applications. One approach to this problem has been to remove the perfect data association requirement for place recognition systems by utilizing robust pose graphs. However, this approach requires a batch-based process and consequently does not solve the problem of real-time navigation in the interim before optimization. We present a novel technique which calibrates the localization system online as the robot moves through an environment, alleviating the need for prior calibration of a localization system. We leverage a robust pose graph optimization to determine a suitable model for our place recognition system which is sensor-invariant and does not require manually-labeled data for threshold tuning. The system continually refines place recognition parameters using sensory data and an optimized pose graph in order to increase the instantaneous precision of the location matches output by the underlying robust pose graph. We evaluate the system using the public New College dataset and a multi-sensor indoor office dataset. Our results indicate that the system produces superior mapping performance to a static place recognition model, and superior instantaneous place recognition performance to a robust pose graph technique.