Multi-LiDAR Placement, Calibration, Co-registration, and Processing on a Subaru Forester for Off-road Autonomous Vehicles Operations

Meadows, W. S., Hudson, C. R., Goodin, C., Dabbiru, L., Powell, B., Doude, M., Carruth, D. W., Islam, M., Ball, J. E., & Tang, B. (2019). Multi-LiDAR Placement, Calibration, Co-registration, and Processing on a Subaru Forester for Off-road Autonomous Vehicles Operations. Proceedings Volume 11009, Autonomous Systems: Sensors, Processing, and Security for Vehicles and Infrastructure 2019. Baltimore, MD. DOI:10.1117/12.2518915.

Abstract

For autonomous vehicles 3D, rotating LiDAR sensors are often critically important towards the vehicle’s ability to sense its environment. Generally, these sensors scan their environment, using multiple laser beams to gather information about the range and the intensity of the reflection from an object. LiDAR capabilities have evolved such that some autonomous systems employ multiple rotating LiDARs to gather greater amounts of data regarding the vehicle’s surroundings. For these multi–LiDAR systems, the placement of the sensors determine the density of the combined point cloud. We perform preliminary research regarding the optimal LiDAR placement strategy on an off–road, autonomous vehicle known as the Halo project. We use the Mississippi State University Autonomous Vehicle Simulator (MAVS) to generate large amounts of labeled LiDAR data that can be usedto train and evaluate a neural network used to process LiDAR data in the vehicle. The trained networks are evaluated and their performance metrics are then used to generalize the performance of the sensor pose. Data generation, training, and evaluation, was performed iteratively to perform a parametric analysis of the effectiveness of various LiDAR poses in the Multi–LiDAR system. We also, describe and evaluate intrinsic and extrinsic calibration methods that are applied in the multi–LiDAR system. In conclusion we found that our simulations are an effective way to evaluate the efficacy of various LiDAR placements based on the performance of the neural network used to process that data and the density of the point cloud in areas of interest.