A Novel Spatial Data Pipeline for Streaming Smart City Data

Abstract

Point cloud data from light detection and ranging (LiDAR) is often used for its spatial qualities, particularly in smart city projects involving vehicles and pedestrians. In this paper, the authors introduce a streaming and an on-demand pipeline for capturing LiDAR data from Velodyne Ultra Pucks placed along nine northern Nevada intersections known as the Living Lab within smart city project for the City of Reno. This pipeline is an iteration of a previously proposed pipeline with several feature enhancements. A streaming point cloud service was implemented to stream Point Cloud Data (PCD), LASzip (LAZ), and Google Draco. Also, two web services were built for the packet capture (PCAP) and ROS 2 bag files that enables acquisition of these formats for LiDAR data. A metadata service tracks edge device states and a GraphQL service interfaces with multiple services across the Living Lab. Draco provided the best processing time and had more options that affected the quality of the point cloud. To evaluate this pipeline, a discussion is provided, with an analysis of the point cloud formats.