Abstract
Abstract. With advances in computing and sensor technologies, onboard systems can deal with a large amount of data and achieve real-time process continuously and accurately. In order to further enhance the performance of positioning, high definition map (HD map) is one of the game changers for future autonomous driving. Instead of directly using Inertial Navigation System and Global Navigation Satellite System (INS/GNSS) navigation solutions to conduct the Direct Geo-referencing (DG) and acquiring 3D mapping information, Simultaneous Localization and Mapping (SLAM) relies heavily on environmental features to derive the position and attitude as well as conducting the mapping at the same time. In this research, the new structure is proposed to integrate the INS/GNSS into LiDAR Odometry and Mapping (LOAM) algorithm and enhance the mapping performance. The first contribution is using the INS/GNSS to provide the short-term relative position information for the mapping process when the LiDAR odometry process is failed. The checking process is built to detect the divergence of LiDAR odometry process based on the residual from correspondences of features and innovation sequence of INS/GNSS. More importantly, by integrating with INS/GNSS, the whole global map is located in the standard global coordinate system (WGS84) which can be shared and employed easily and seamlessly. In this research, the designed land vehicle platform includes commercial INS/GNSS integrated product as a reference, relatively low-cost and lower grade INS system and Velodyne LiDAR with 16 laser channels, respectively. The field test is conducted from outdoor to the indoor underground parking lot and the final solution using the proposed method has a significant improvement as well as building a more accurate and reliable map for future use.
Highlights
To further enhance the performance of positioning, high definition map (HD map) is one of the game changers for future autonomous driving
This paper develops the inertial navigation system (INS)-aiding 3D LiDAR-Simultaneous Localization and Mapping (SLAM) based on the LiDAR odometry and mapping method
As mentioned in the experiment section, this paper utilizes commercial product and software to generate the ground truth. This ground truth is compared with the proposed method, conventional INS/Global Navigation Satellite System (GNSS) and pure SLAM results
Summary
To further enhance the performance of positioning, high definition map (HD map) is one of the game changers for future autonomous driving. HD map should allow the autonomous systems to recognize either the vehicle is on the highway or under the highway, on the road or in the underground parking lot. This map needs to be generated in the global coordinate system to share consistent information with other vehicles. In order to help the self-driving car successfully and safely arrive at the destination, all of the features or the geospatial information should have the sub-meter accuracy (Farrell et al, 2016). It is not easy to generate 3D maps corresponding to the sub-meter accuracy, in Global Navigation Satellite System (GNSS) signal outage environment
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