ALOT: Augmented Localization with Obstacle Tracking

Augmented Localization with Obstacle Tracking (ALOT) is a pipeline for localization, involving closed-loop feedback between an obstacle tracker and a particle filter localization. The tracker tracks and labels dynamic obstacles it sees and uses historic information to predict positions of dynamic obstacles at the current time-step. Following up on this, the tracker uses the current observation along with predicted obstacle positions to proposes ego poses for localization. The localization method in ALOTemploys a particle filter. During scan matching, it removes dynamic obstacles from the scan using information obtained from the tracker. Particles are weighted once during scanmatching, and a second time with ego-pose proposals provided by the tracker. Upon reconstructing the ego-pose belief, the particle filter localization provides a feedback to the tracker with themost likely ego-pose to allow the tracker to update its tracking and further propose ego-poses at the next time-step. ALOT is tested on real-world data collected in a laboratory. In lowtomoderately dynamic environments, it achieves an average positional and heading errors of 0.171m and 1.63 degrees respectively. When run in larger crowds, ALOT has positional and heading errors of 0.467m and 4.784 degrees.