Abstract
In this paper, we proposed a multi-sensor integrated navigation system composed of GNSS (global navigation satellite system), IMU (inertial measurement unit), odometer (ODO), and LiDAR (light detection and ranging)-SLAM (simultaneous localization and mapping). The dead reckoning results were obtained using IMU/ODO in the front-end. The graph optimization was used to fuse the GNSS position, IMU/ODO pre-integration results, and the relative position and relative attitude from LiDAR-SLAM to obtain the final navigation results in the back-end. The odometer information is introduced in the pre-integration algorithm to mitigate the large drift rate of the IMU. The sliding window method was also adopted to avoid the increasing parameter numbers of the graph optimization. Land vehicle tests were conducted in both open-sky areas and tunnel cases. The tests showed that the proposed navigation system can effectually improve accuracy and robustness of navigation. During the navigation drift evaluation of the mimic two-minute GNSS outages, compared to the conventional GNSS/INS (inertial navigation system)/ODO integration, the root mean square (RMS) of the maximum position drift errors during outages in the proposed navigation system were reduced by 62.8%, 72.3%, and 52.1%, along the north, east, and height, respectively. Moreover, the yaw error was reduced by 62.1%. Furthermore, compared to the GNSS/IMU/LiDAR-SLAM integration navigation system, the assistance of the odometer and non-holonomic constraint reduced vertical error by 72.3%. The test in the real tunnel case shows that in weak environmental feature areas where the LiDAR-SLAM can barely work, the assistance of the odometer in the pre-integration is critical and can effectually reduce the positioning drift along the forward direction and maintain the SLAM in the short-term. Therefore, the proposed GNSS/IMU/ODO/LiDAR-SLAM integrated navigation system can effectually fuse the information from multiple sources to maintain the SLAM process and significantly mitigate navigation error, especially in harsh areas where the GNSS signal is severely degraded and environmental features are insufficient for LiDAR-SLAM.
Highlights
Navigation systems that integrate multiple sensors and provide information for high data rates, high accuracy, and all-weather capability will become the core components of platforms such as autonomous driving and intelligent robots in the near future
The proposed GNSS/IMU/ODO/LiDAR-SLAM integrated navigation system can effectually fuse the information from multiple sources to maintain the SLAM process and significantly mitigate navigation error, especially in harsh areas where the GNSS signal is severely degraded and environmental features are insufficient for LiDAR-SLAM
Compared with the GNSS/INS/ODO integrated navigation system, the position error root mean square (RMS) was reduced by 62.8%, 72.3%, and 52.1%; the heading error RMS was reduced by 62.1%; and the roll and pitch errors were equivalent
Summary
Navigation systems that integrate multiple sensors and provide information for high data rates, high accuracy, and all-weather capability will become the core components of platforms such as autonomous driving and intelligent robots in the near future. In withwith the improvement of computer performance, SLAM Years, the improvement of computer performance, SLAM (simultaneous and mapping) technology that uses remote sensors for navigation has been widely used in vehicle localization and mapping) technology that uses remote sensors for navigation has been widely used navigation. SLAM mainly obtains the pose by matching the observed environmental features with in vehicle navigation. SLAM mainly obtains the pose by matching the observed environmental the feature map moving and simultaneously updates the feature map achieve autonomous features with thewhile feature map while moving and simultaneously updates theto feature map to achieve positioning [2]
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