LiMoSeg: Real-time Bird's Eye View based LiDAR Motion Segmentation
2021
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 two 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.
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