Projection and integration of connected-infrastructure LiDAR sensing data in a global coordinate

Abstract Recently, roadside Light Detection and Ranging (LiDAR) has been deployed for different transportation applications such as high-resolution-micro-traffic data collection, vehicle–pedestrian safety evaluation, and driver’s behavior analysis. An ideal LiDAR-enhanced traffic infrastructure system needs multiple LiDAR sensors deployed around intersections and along road segments, which generate a seamless coverage of intersections or arterials. To obtain continuous and complete traffic data, the integration method is much more important to extend the data range and improve the density of scanned points. In this research, an innovative approach based on the GPS mapping method was present to automatically integrate data collected by different LiDAR sensors in a global coordinate. In this method, the raw data collected by multiple LiDAR sensors are used as input, at least 4 reference points collected by GPS devices are needed for each LiDAR sensor, then a transformation step is applied to transform all the LiDAR points into the Earth-centered, Earth-fixed coordinate system. After obtaining all the LiDAR points in the global coordinate system, an Iterative Closest Point (ICP) method was used to reduce the errors caused by data collection and calculation. The sensitivity analysis part provided the best number for the reference points collection. At last, the data collected at two sites (Evans & McCarran intersection and Blue parking lot of University of Nevada, Reno (UNR) were selected to verify the method. The testing results showed that the proposed method has a high level of automation and improved accuracy.