LiDAR sensors can produce point clouds with precise 3D depth information that is essential for autonomous vehicles and robotic systems. As a perception task, point cloud clustering algorithms can be applied to segment the points into object instances. In this brief, we propose a novel, hardware-friendly fast channel clustering (FCC) algorithm that achieves state-of-the-art performance when evaluated using KITTI panoptic segmentation benchmark. Furthermore, an efficient, pipeline hardware architecture is proposed to implement the FCC algorithm on an FPGA. Experiments show that the hardware design can process each LiDAR frame with 64 channels, 2048 horizontal resolution at various point sparsity in 1.93 ms, which is more than 471.5 times faster than running on the CPU. The code will be released to the public via GitHub.

中文翻译：

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激光雷达点云的实时快速通道聚类

LiDAR 传感器可以生成具有精确 3D 深度信息的点云，这对于自动驾驶汽车和机器人系统至关重要。作为感知任务，可以应用点云聚类算法将点分割成对象实例。在本简报中，我们提出了一种新颖的、硬件友好的快速通道聚类 (FCC) 算法，该算法在使用 KITTI 全景分割基准进行评估时实现了最先进的性能。此外，提出了一种高效的流水线硬件架构来在 FPGA 上实现 FCC 算法。实验表明，该硬件设计可以在1.93 ms内处理64通道、2048水平分辨率下各点稀疏度的每个LiDAR帧，比在CPU上运行快471.5倍以上。代码将通过 GitHub 向公众发布。