Clustering objects from the LiDAR point cloud is an important research problem with many applications such as autonomous driving. To meet the real-time requirement, existing research proposed to apply the connected-component-labeling (CCL) technique on LiDAR spherical range image with a heuristic condition to check if two neighbor points are connected. However, LiDAR range image is different from a binary image which has a deterministic condition to tell if two pixels belong to the same component. The heuristic condition used on the LiDAR range image only works empirically, which suggests the LiDAR clustering algorithm should be robust to potential failures of the empirical heuristic condition. To overcome this challenge, this paper proposes a divide-and-merge LiDAR clustering algorithm. This algorithm firstly conducts clustering in each evenly divided local region, then merges the local clustered small components by voting on edge point pairs. Assuming there are $N$ LiDAR points of objects in total with $m$ divided local regions, the time complexity of the proposed algorithm is $O(N)+O(m^2)$. A smaller $m$ means the voting will involve more neighbor points, but the time complexity will become larger. So the $m$ controls the trade-off between the time complexity and the clustering accuracy. A proper $m$ helps the proposed algorithm work in real-time as well as maintain good performance. We evaluate the divide-and-merge clustering algorithm on the SemanticKITTI panoptic segmentation benchmark by cascading it with a state-of-the-art semantic segmentation model. The final performance evaluated through the leaderboard achieves the best among all published methods. The proposed algorithm is implemented with C++ and wrapped as a python function. It can be easily used with the modern deep learning framework in python.
翻译:从 LiDAR 点云中分组对象是一个重要的研究问题, 包括自动驱动等许多应用程序。 为了满足实时要求, 现有的研究建议对 LiDAR 球形图像应用连接组件标签技术( CCL), 以超速状态检查连接两个相邻点。 但是, LiDAR 范围图像不同于二进制图像, 它具有确定性条件来判断两个像素是否属于同一个组件。 liDAR 范围图像中使用的超常状态只能用经验来操作。 这表明 LiDAR 群集算算法对于经验性平流状态的潜在失败应该具有很强性能。 为了克服这一挑战, 本文建议对LIDAR 球形范围图像应用一个有超常性功能, 检查两个相近点连接点的图像。 假设在本地区域里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程数, 内程内, 内, 内程内, 内, 里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程内, 里程里程里程里程里程里程里程里程里程里程里程里程内, 里程内, 里程内, 里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程里程内