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      GNSS/IMU/ODO/LiDAR-SLAM Integrated Navigation System Using IMU/ODO Pre-Integration

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      , * ,
      Sensors (Basel, Switzerland)
      MDPI
      GNSS, IMU, ODO, LiDAR-SLAM, pre-integration, graph optimization

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          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.

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          Most cited references29

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          Simultaneous localization and mapping: part I

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            VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator

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              Real-time loop closure in 2d lidar slam

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                20 August 2020
                September 2020
                : 20
                : 17
                : 4702
                Affiliations
                GNSS Research Center, Wuhan University, 129 Luoyu Road, Wuhan 430079, China; changlesgg@ 123456whu.edu.cn (L.C.); liutianyi@ 123456whu.edu.cn (T.L.)
                Author notes
                [* ]Correspondence: xjniu@ 123456whu.edu.cn ; Tel.: +86-186-0270-8312
                Author information
                https://orcid.org/0000-0002-5591-0859
                Article
                sensors-20-04702
                10.3390/s20174702
                7506683
                12c02086-0e1a-45e5-aeaf-8947b6da17f3
                © 2020 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 29 July 2020
                : 18 August 2020
                Categories
                Article

                Biomedical engineering
                gnss,imu,odo,lidar-slam,pre-integration,graph optimization
                Biomedical engineering
                gnss, imu, odo, lidar-slam, pre-integration, graph optimization

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