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.

Abstract. As research on autonomous driving deepens, High-definition Maps (HD Maps) have gradually become an auxiliary information for the new generation of autonomous driving technology. Compared to traditional electronic navigation maps, HD Maps have higher accuracy requirements and more information. Multi-road environment information and road elements are included. In the production of HD Maps, the on-board Mobile Laser Scanning (MLS) system has the ability to quickly collect environmental information, with high precision, thus making the system a widely used data collection method today. However, subsequent map building, digitization, and other mapping work still rely on manual operation, which is time-consuming and laborious. Therefore, this research is dedicated to developing a semi-automatic algorithm to generate HD Maps from the acquired point cloud data. This research focuses on the extraction of road surface markings, using the Cloth Simulation Filter (CSF) to obtain the road surface point cloud to improve the extraction efficiency. The road markings are extracted using the characteristic of high intensity values, and the commonly used Otsu threshold filter in image processing is used to extract point clouds with high reflectance intensity, eliminating the need for manual setting of point clouds. And based on geometric conditions, the objects are classified, such as arrow lines, pedestrian crossings, stop lines, and lane lines, which are convenient for further mapping HD Maps.

Abstract. To meet the autopilot demand of autonomous vehicle, higher automation level accompanies with higher consideration of safety factor to improve navigation accuracy. Moreover, it shall be stable under diverse environment, e.g., semi-open sky, urban, traffic jam, etc, where conventional navigation methods, the Inertial Measurement Unit (IMU) and global Navigation Satellite System (GNSS), might be limited. Thus, auxiliary sensor, the light detection and ranging (LiDAR), is applied to provide additional information to assist navigation under GNSS challenging environment, and fulfil Simultaneous Localization and Mapping (SLAM). To initially align the LiDAR point cloud, initial pose is generated by Extended Kalman Filter (EKF) through Loosely Coupled (LC) scheme, assisting with motion constraints, including Zero Velocity Update (ZUPT), Non-Holonomic Constraints (NHC), and Zero Integrated Heading Rate (ZIHR) function. With point cloud after initial alignment, registration method applied in this research is point to distribution based-Normal Distribution Transform (P2D-NDT), with scan to dynamic map matching. However, pure LiDAR-SLAM estimated solution remains faults in each measurement, which will propagate through computation and leads to false navigation outcome. Therefore, this paper proposed Fault Detection, Isolation, and Exclusion (FDIE) scheme to exclude the faults in each step of LiDAR-SLAM process. The final estimated solution is compared to robust reference data, the results turn out that convention navigation method work well under stable GNSS signal environment, while significant accuracy enhancement is achieved with NDT and FDE under large initial pose offset, such as GNSS signal blocked area.

Abstract. Outdoor positioning requires a reliable solution that can work in environments where satellite signals are often blocked or degraded. Global Navigation Satellite System (GNSS) is a common choice, but it may not provide accurate results for land vehicles. To address this challenge, this research proposes a multi-sensor integrated system for vehicle navigation that combines GNSS with other sensors. The system uses Extended Kalman Filter (EKF) to fuse the data from different sources and improve the navigation performance. The algorithm targets to provide seamless navigation for urban environments as well as various indoor environments fields with INS/GNSS/VIO aiding integrated solutions. The experimental vehicle of this research is equipped with a tactical-grade inertial sensing measurement unit (IMU) as the test system, a self-designed and assembled visual platform, which includes a camera with a time synchronization protocol and a low-cost IMU. Also, both indoor experimental fields and outdoor urban scenarios with different high challenging were tested to verify the developed algorithm. To evaluate the performance of the proposed real-time navigation system, we use a high-accuracy navigation-grade system as a reference, which provides a stable and reliable trajectory. The result indicates that using the GNSS RTK solution with VIO aiding integration scheme reduced the RMS errors in long outage (450 sec, 1812 m) by 87.4% and 79.9% in position and velocity error, respectively. In urban scenario, the along-track/cross-track maximum errors can achieve 1.4 m / 1.5 m. Overall, these contribute to the development of real-time navigation systems for self-driving vehicle in the future.