KINEMATIC CALIBRATION USING LOW-COST LiDAR SYSTEM FOR MAPPING AND AUTONOMOUS DRIVING APPLICATIONS

Abstract. More recently, mapping sensors for land-based Mobile Mapping Systems (MMSs) have combined cameras and laser scanning measurements defined as Light Detection and Ranging (LiDAR), or laser scanner together. These mobile laser scanning systems (MLS) can be used in dynamic environments and are able of being adopted in traffic-related applications, such as the collection of road network databases, inventory of traffic sign and surface conditions, etc. However, most LiDAR systems are expensive and not easy to access. Moreover, due to the increasing demand of the autonomous driving system, the low-cost LiDAR systems, such as Velodyne or SICK, have become more and more popular these days. These kinds of systems do not provide the total solution. Users need to integrate with Inertial Navigation System/ Global Navigation Satellite System (INS/GNSS) or camera by themselves to meet their requirement. The transformation between LiDAR and INS frames must be carefully computed ahead of conducting direct geo-referencing. To solve these issues, this research proposes the kinematic calibration model for a land-based INS/GNSS/LiDAR system. The calibration model is derived from the direct geo-referencing model and based on the conditioning of target points where lie on planar surfaces. The calibration parameters include the boresight and lever arm as well as the plane coefficients. The proposed calibration model takes into account the plane coefficients, laser and INS/GNSS observations, and boresight and lever arm. The fundamental idea is the constraint where geo-referenced point clouds should lie on the same plane through different directions during the calibration. After the calibration process, there are two evaluations using the calibration parameters to enhance the performance of proposed applications. The first evaluation focuses on the direct geo-referencing. We compared the target planes composed of geo- referenced points before and after the calibration. The second evaluation concentrates on positioning improvement after taking aiding measurements from LiDAR- Simultaneously Localization and Mapping (SLAM) into INS/GNSS. It is worth mentioning that only one or two planes need to be adopted during the calibration process and there is no extra arrangement to set up the calibration field. The only requirement for calibration is the open sky area with the clear plane construction, such as wall or building. Not only has the contribution in MMSs or mapping, this research also considers the self-driving applications which improves the positioning ability and stability.