Bormann, PascalDorra, TobiasStahl, BastianFellner, Dieter W.Peter VangorpMartin J. Turner2022-08-162022-08-162022978-3-03868-188-5https://doi.org/10.2312/cgvc.20221173https://diglib.eg.org:443/handle/10.2312/cgvc20221173We 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.CCS Concepts: Information systems → Geographic information systems; Mobile information processing systems; Data structures; Computing methodologies → Point-based models; Vector / streaming algorithmsInformation systems → Geographic information systemsMobile information processing systemsData structuresComputing methodologies → Pointbased modelsVector / streaming algorithms10.2312/cgvc.2022117365-739 pages