Real-time Indexing of Point Cloud Data During LiDAR Capture

Abstract

We introduce a software system that is capable of indexing point cloud data in real-time as it is being captured by a LiDAR (Light Detection and Ranging) sensor. Our system extends the popular MNO (modifiable nested octree) structure so that it can be built progressively without knowing the bounding box of the point cloud. Using a task-based parallel algorithm incoming points are continuously processed and distributed to the octree nodes using grid-based sampling. Different task priority functions enable prioritization of either high point throughput or low latency. We provide a reference implementation of this system and evaluate it using both a synthetic and a real-world test scenario. The synthetic test demonstrates good scalability up to 16 threads, with maximum point throughputs of up to 1.8 million points per second. These numbers are verified on a sensor system using a Velodyne VLP-16 LiDAR sensor, where our system is able to index all data produced by the scanner in real-time.