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Moving object detection and segmentation is an essential task in theAutonomous Driving pipeline. Detecting and isolating static and movingcomponents of a vehicle's surroundings are particularly crucial in pathplanning and localization tasks. This paper proposes a novel real-timearchitecture 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 thesignificant class imbalance between static and moving objects. We achieve thisby artificially synthesizing moving objects by cutting and pasting staticvehicles. We demonstrate a low latency of 8 ms on a commonly used automotiveembedded platform, namely Nvidia Jetson Xavier. To the best of our knowledge,this is the first work directly performing motion segmentation in LiDAR BEVspace. We provide quantitative results on the challenging SemanticKITTIdataset, and qualitative results are provided in https://youtu.be/2aJ-cL8b0LI.

LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation