Real-Time LiDAR Point-Cloud Moving Object Segmentation for Autonomous Driving

Abstract

The key to autonomous navigation in unmanned systems is the ability to recognize static and moving objects in the environment and to support the task of predicting the future state of the environment, avoiding collisions, and planning. However, because the existing 3D LiDAR point-cloud moving object segmentation (MOS) convolutional neural network (CNN) models are very complex and have large computation burden, it is difficult to perform real-time processing on embedded platforms. In this paper, we propose a lightweight MOS network structure based on LiDAR point-cloud sequence range images with only 2.3 M parameters, which is 66% less than the state-of-the-art network. When running on RTX 3090 GPU, the processing time is 35.82 ms per frame and it achieves an intersection-over-union(IoU) score of 51.3% on the SemanticKITTI dataset. In addition, the proposed CNN successfully runs the FPGA platform using an NVDLA-like hardware architecture, and the system achieves efficient and accurate moving-object segmentation of LiDAR point clouds at a speed of 32 fps, meeting the real-time requirements of autonomous vehicles.

FusionVision: A Comprehensive Approach of 3D Object Reconstruction and Segmentation from RGB-D Cameras Using YOLO and Fast Segment Anything

“…The significance of point-cloud processing has surged across various domains, such as robotics [1,2], medical field [3,4], autonomous driving [5,6], metrology [7][8][9], etc. Over the past few years, advancements in vision sensors have led to remarkable improvements, enabling these sensors to provide real-time 3D measurements of the surroundings while maintaining decent accuracy [10,11].…”

Stitching Locally Fitted T-Splines for Fast Fitting of Large-Scale Freeform Point Clouds

“…In precision optical metrology, point cloud data processing enables high-precision shape measurement and surface reconstruction, which is important for applications requiring very high measurement accuracy, such as engineering quality control, product inspection, and reverse engineering [ 1 , 2 , 3 ]. In the realm of smart sensing, point cloud data processing finds critical utility in environmental sensing and obstacle detection, addressing applications of paramount importance [ 4 , 5 , 6 ]. Consequently, point cloud data processing plays a crucial role in both smart sensing and precision optical metrology, offering essential tools and a data foundation for resolving intricate challenges.…”