PointSLOT: Real-Time Simultaneous Localization and Object Tracking for Dynamic Environment

Abstract

The applicability of SLAM algorithms is largely limited to the assumption of scene rigidity. Moreover, dynamic object perception is an essential technique for many robotic applications such as autonomous driving and multi-robot collaboration. In this letter, we present PointSLOT, an online, real-time and general stereo system that simultaneously performs SLAM and dynamic object tracking without any artificial priors. Unlike previous SLOT systems, we explicitly identify moving and stationary objects by computing the object motion probabilities, such that features from static objects are utilized to improve the camera pose estimation instead of treating all object regions as outliers. Based on the camera-centric parameterization for the object pose, the trajectories and dynamic landmarks of multi-keyframe objects are efficiently computed through an object-based bundle adjustment approach. The evaluations on KITTI datasets and real-world experiments show that the proposed system achieves comparable results to state-of-the-art solutions on both camera motion estimation and dynamic object tracking. The open-source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/pkzhou/PointSLOT</uri> .