LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation
This addresses the need for efficient moving object detection in autonomous vehicles, though it is incremental as it builds on existing motion segmentation tasks.
The paper tackles real-time motion segmentation of LiDAR data for autonomous driving by proposing a novel architecture that classifies pixels as static or moving using three successive scans in Bird's Eye View, achieving a low latency of 8 ms on an embedded platform.
Moving object detection and segmentation is an essential task in the Autonomous Driving pipeline. Detecting and isolating static and moving components of a vehicle's surroundings are particularly crucial in path planning and localization tasks. This paper proposes a novel real-time architecture for motion segmentation of Light Detection and Ranging (LiDAR) data. We use three successive scans of LiDAR data in 2D Bird's Eye View (BEV) representation to perform pixel-wise classification as static or moving. Furthermore, we propose a novel data augmentation technique to reduce the significant class imbalance between static and moving objects. We achieve this by artificially synthesizing moving objects by cutting and pasting static vehicles. We demonstrate a low latency of 8 ms on a commonly used automotive embedded platform, namely Nvidia Jetson Xavier. To the best of our knowledge, this is the first work directly performing motion segmentation in LiDAR BEV space. We provide quantitative results on the challenging SemanticKITTI dataset, and qualitative results are provided in https://youtu.be/2aJ-cL8b0LI.