InsClustering: Instantly Clustering LiDAR Range Measures for Autonomous Vehicle

TL;DR

This paper introduces InsClustering, a rapid 3D LiDAR point cloud segmentation method for autonomous vehicles that processes raw data packets in under 1ms, significantly enhancing speed while maintaining accuracy.

Contribution

The paper presents a novel, field-tested algorithm for instant 3D LiDAR data clustering, reducing processing delay to under 1ms for autonomous driving applications.

Findings

Processing delay less than 1ms on Velodyne UltraPuck

Significant speed improvement over existing methods

Maintains good clustering accuracy in real-world tests

Abstract

LiDARs are usually more accurate than cameras in distance measuring. Hence, there is strong interest to apply LiDARs in autonomous driving. Different existing approaches process the rich 3D point clouds for object detection, tracking and recognition. These methods generally require two initial steps: (1) filter points on the ground plane and (2) cluster non-ground points into objects. This paper proposes a field-tested fast 3D point cloud segmentation method for these two steps. Our specially designed algorithms allow instantly process raw LiDAR data packets, which significantly reduce the processing delay. In our tests on Velodyne UltraPuck, a 32 layers spinning LiDAR, the processing delay of clustering all the $360^\circ$ LiDAR measures is less than 1ms. Meanwhile, a coarse-to-fine scheme is applied to ensure the clustering quality. Our field experiments in public roads have shown that the proposed method significantly improves the speed of 3D point cloud clustering whilst maintains good accuracy.