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.
翻译:移动物体的探测和分离是自动驱动管道中的一项基本任务。 检测和隔离车辆周围静态和移动部件对于路径规划和定位任务尤为重要。 本文建议建立一个新型的实时结构, 用于光探测和测距( LiDAR) 数据的运动分解。 我们在 2D Bird 眼睛视图中连续进行两次LIDAR 数据扫描, 以进行静态或移动等离子分类。 此外, 我们提出一种新的数据增强技术, 以减少静态和移动物体之间重大的等级不平衡。 我们通过切割和粘贴静车辆来人工合成移动物体。 我们展示了通用汽车嵌入平台( 即 Nvidia Jetson Xavier) 的低纬度为 8 ms 。 根据我们所知, 这是直接在 LiDAR BEV 空间进行运动分解的第一个工作。 我们提供了关于具有挑战性的 SmanticKITTI 数据集的量化结果, 并在 https://youtu. bea/ cL8L0LLI 中提供定性结果 。