DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization

• URL: http://arxiv.org/abs/2202.11431v1
• Date: Wed, 23 Feb 2022 11:22:43 GMT
• Title: DL-SLOT: Dynamic Lidar SLAM and Object Tracking Based On Graph Optimization
• Authors: Xuebo Tian, Junqiao Zhao, Chen Ye
• Abstract summary: Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. We perform SLAM and object tracking simultaneously in this framework, which significantly improves the robustness and accuracy of SLAM in highly dynamic road scenarios.
• Score: 2.889268075288957
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ego-pose estimation and dynamic object tracking are two key issues in an autonomous driving system. Two assumptions are often made for them, i.e. the static world assumption of simultaneous localization and mapping (SLAM) and the exact ego-pose assumption of object tracking, respectively. However, these assumptions are difficult to hold in highly dynamic road scenarios where SLAM and object tracking become correlated and mutually beneficial. In this paper, DL-SLOT, a dynamic Lidar SLAM and object tracking method is proposed. This method integrates the state estimations of both the ego vehicle and the static and dynamic objects in the environment into a unified optimization framework, to realize SLAM and object tracking (SLOT) simultaneously. Firstly, we implement object detection to remove all the points that belong to potential dynamic objects. Then, LiDAR odometry is conducted using the filtered point cloud. At the same time, detected objects are associated with the history object trajectories based on the time-series information in a sliding window. The states of the static and dynamic objects and ego vehicle in the sliding window are integrated into a unified local optimization framework. We perform SLAM and object tracking simultaneously in this framework, which significantly improves the robustness and accuracy of SLAM in highly dynamic road scenarios and the accuracy of objects' states estimation. Experiments on public datasets have shown that our method achieves better accuracy than A-LOAM.

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