HIGH-DEFINITION POINT CLOUD MAP-BASED 3D LiDAR-IMU CALIBRATION FOR SELF-DRIVING APPLICATIONS

Abstract

The multi-sensor fusion scheme has become more and more popular these days with its great potential to estimate reliable navigation information for the modern development in automated driving system (ADS) and mobile mapping systems (MMS). Since these systems are combined with numerous navigation sensors, thus their geometric relationship should be precisely known. This study focuses on practical aspects when calibrating LiDAR-IMU mounting parameters (lever-arms and bore-sight angles) in land-based MMS. This calibration model is based on expressing the mounting parameters within the direct georeferencing equation for each epoch time and conditioning a set of INS/GNSS and LiDAR navigation solutions to lie on it. There is no need for a required information about the planar features in the calibration field as part of the unknowns. Such conditions are only benefitable in the residential area where the presence of sufficient planes in form of building is abundant. We present an approach for recovery the mounting parameters by conditioning the high-definition (HD) point cloud map-based LiDAR information and INS/GNSS navigation solutions through the least-squares solutions. The presented results and discussion mainly focus on practical examples with data from land-based MMS. Preliminary results indicate that correct calibration parameters are not only capable to improve the performance of point cloud georeferencing but also dramatically provide reliable performance evaluation of navigation estimation. Moreover, these findings show that the studied method is not only applicable in the featureless environment but also in its practicality to the self-driving applications.