Abstract
LiDAR-based Multiple Object Detection and Tracking (MODT) is one of the essential tasks in autonomous driving. Since MODT is directly related to the safety of an autonomous vehicle, it is critical to provide reliable information about the surrounding objects. For that reason, we propose a semantic point cloud-based adaptive MODT system for autonomous driving. Semantic point clouds emerge with advances in deep learning-based Point Cloud Semantic Segmentation (PCSS), which assigns semantic information to each point in the point cloud of LiDAR. This semantic information provides several advantages to the MODT system. First, any point corresponding to any static object can be filtered. Because the class information assigned to each point can be directly utilized, filtering is possible without any modeling. Second, the class information of an object can be inferred without any special classification process because the class information is provided from the semantic point cloud. Finally, the clustering and tracking module can consider unique dimensional and dynamic characteristics based on class information. We utilize the Carla simulator and KITTI dataset to verify our method by comparing several existing algorithms. In conclusion, the performance of the proposed algorithm is improved by about 176% on average compared to the existing algorithm.
Highlights
The Point Cloud Semantic Segmentation (PCSS) research field is growing rapidly with the development of deep learning technology
There are many improvements compared to the Interacting Multiple Model (IMM)-Unscented Kalman Filter (UKF)-Joint Probabilistic Data Association (JPDA) algorithm selected as the basic algorithm, and the effect of the class adaptation module can be shown
In this paper, a class-adaptive Multiple Object Detection and Tracking (MODT) framework based on semantic point cloud was proposed
Summary
The Point Cloud Semantic Segmentation (PCSS) research field is growing rapidly with the development of deep learning technology. Offline PCSS is mainly used in applications that do not require real-time characteristics, such as map construction. Online PCSS performs semantic segmentation of the point cloud at a real-time level. Due to real-time capability, online PCSS can be applied to various autonomous driving tasks such as object detection, SLAM, and trajectory planning. According to the semantic KITTI benchmark, one of the popular benchmarks for PCSS, the 2D CNN-based online network with the highest performance has mIoU of 59.9 %, as shown in Table.. We show that progress is being made actively enough to produce reliable performance within a short period Due to this real-time capability and reliable performance characteristics, PCSS can be applied to many applications required to perceive the surrounding environments, such as object detection, recognition, and localization. This paper will focus on applying online PCSS to Multiple Object Detection and Tracking (MODT)
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