Continuous-Time Estimation For Dynamic Obstacle Tracking

This paper reports on a system for dynamic obstacle tracking for autonomous vehicles. In this work, we seek to simultaneously estimate both the trajectory of the obstacle and the obstacle's shape. These two tasks are inherently coupled-given only noisy partial views, one cannot accurately estimate the trajectory of an obstacle if its shape is unknown, nor can one estimate its shape without knowing its trajectory. To address this challenge, we note that simultaneous localization and mapping (SLAM), where a robot must build a map of the environment while localizing itself within the map, presents similar challenges. By treating the obstacle's shape as a "map" in the obstacle's moving reference frame, we can formulate the obstacle tracking and shape estimation similarly to SLAM. Additionally, we use a continuous time estimation framework to incorporate sensor data that is collected at a fast rate (e.g., light detection and ranging (LIDAR)). Using these methods, we are able to obtain smooth trajectories and crisp point clouds for tracked obstacles. We test our proposed tracker on real-world data collected by our autonomous vehicle platform and demonstrate that it produces improved results when compared to a standard centroid-based extended Kalman filter (EKF) tracker.