A Switching-Coupled Backend for Simultaneous Localization and Dynamic Object Tracking

Abstract

Simultaneous localization and object tracking (SLOT) is essentially important for autonomous systems. Tightly-coupled and loosely-coupled methods are two commonly used back-end frameworks for the state-of-the-art solutions of SLOT problem. However, some inherent limitations exist in these two frameworks. In particular, the tightly-coupled method is usually disturbed by the poor observations of some dynamic objects, and the performance of a loosely-coupled one completely depends on that of classical static simultaneous localization and mapping (SLAM) process. Motivated by these observations, we propose a novel switching-coupled back-end solution and theoretically derive its concrete form using probability representation. Based on the switching strategy and the proposed objects classification criteria where the object uncertainty, observation quality and prior information are jointly considered, the dynamic objects’ states are flexibly coupled with camera s state and static landmarks’ states. For implementation, the measurement constraints of “good” dynamic objects and static landmarks are simultaneously leveraged to perform SLAM and good object tracking (SLAMGOT) process, and those of “bad” objects are used for bad object tracking (BOT) process based on the obtained camera state. Extensive evaluations on synthetic scenes, KITTI datasets and real-world experiments demonstrate the performance of the proposed method.