Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems
The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system fo...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-01-01
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| Series: | Micromachines |
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| Online Access: | http://www.mdpi.com/2072-666X/6/2/196 |
| _version_ | 1818028748013830144 |
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| author | Shifei Liu Mohamed Maher Atia Yanbin Gao Aboelmagd Noureldin |
| author_facet | Shifei Liu Mohamed Maher Atia Yanbin Gao Aboelmagd Noureldin |
| author_sort | Shifei Liu |
| collection | DOAJ |
| description | The accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs) that fuses measurements from a MEMS-grade gyroscope, speed measurements and a light detection and ranging (LiDAR) sensor. A computationally efficient weighted line extraction method is introduced, where the LiDAR intensity measurements are used, such that the random range errors and systematic errors due to surface reflectivity in LiDAR measurements are considered. The vehicle pose change is obtained from LiDAR line feature matching, and the corresponding pose change covariance is also estimated by a weighted least squares-based technique. The estimated LiDAR-based pose changes are applied as periodic updates to the Inertial Navigation System (INS) in an innovative extended Kalman filter (EKF) design. Besides, the influences of the environment geometry layout and line estimation error are discussed. Real experiments in indoor environment are performed to evaluate the proposed algorithm. The results showed the great consistency between the LiDAR-estimated pose change covariance and the true accuracy. Therefore, this leads to a significant improvement in the vehicle’s integrated navigation accuracy. |
| first_indexed | 2024-12-10T05:08:43Z |
| format | Article |
| id | doaj.art-b8e88f368ffd46399734c58ce9083f58 |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-12-10T05:08:43Z |
| publishDate | 2015-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-b8e88f368ffd46399734c58ce9083f582022-12-22T02:01:10ZengMDPI AGMicromachines2072-666X2015-01-016219621510.3390/mi6020196mi6020196Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation SystemsShifei Liu0Mohamed Maher Atia1Yanbin Gao2Aboelmagd Noureldin3College of Automation, Harbin Engineering University, 145 Nantong St., Nangang District, Harbin 150001, ChinaDepartment of Electrical and Computer Engineering, Royal Military College of Canada, P.O. Box 17000, Station Forces, Kingston, ON K7K 7B4, CanadaCollege of Automation, Harbin Engineering University, 145 Nantong St., Nangang District, Harbin 150001, ChinaDepartment of Electrical and Computer Engineering, Royal Military College of Canada, P.O. Box 17000, Station Forces, Kingston, ON K7K 7B4, CanadaThe accurate estimation of measurements covariance is a fundamental problem in sensors fusion algorithms and is crucial for the proper operation of filtering algorithms. This paper provides an innovative solution for this problem and realizes the proposed solution on a 2D indoor navigation system for unmanned ground vehicles (UGVs) that fuses measurements from a MEMS-grade gyroscope, speed measurements and a light detection and ranging (LiDAR) sensor. A computationally efficient weighted line extraction method is introduced, where the LiDAR intensity measurements are used, such that the random range errors and systematic errors due to surface reflectivity in LiDAR measurements are considered. The vehicle pose change is obtained from LiDAR line feature matching, and the corresponding pose change covariance is also estimated by a weighted least squares-based technique. The estimated LiDAR-based pose changes are applied as periodic updates to the Inertial Navigation System (INS) in an innovative extended Kalman filter (EKF) design. Besides, the influences of the environment geometry layout and line estimation error are discussed. Real experiments in indoor environment are performed to evaluate the proposed algorithm. The results showed the great consistency between the LiDAR-estimated pose change covariance and the true accuracy. Therefore, this leads to a significant improvement in the vehicle’s integrated navigation accuracy.http://www.mdpi.com/2072-666X/6/2/196LiDARMEMS-based INSUGVindoor navigationcovariance estimationmulti-sensor integration |
| spellingShingle | Shifei Liu Mohamed Maher Atia Yanbin Gao Aboelmagd Noureldin Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems Micromachines LiDAR MEMS-based INS UGV indoor navigation covariance estimation multi-sensor integration |
| title | Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems |
| title_full | Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems |
| title_fullStr | Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems |
| title_full_unstemmed | Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems |
| title_short | Adaptive Covariance Estimation Method for LiDAR-Aided Multi-Sensor Integrated Navigation Systems |
| title_sort | adaptive covariance estimation method for lidar aided multi sensor integrated navigation systems |
| topic | LiDAR MEMS-based INS UGV indoor navigation covariance estimation multi-sensor integration |
| url | http://www.mdpi.com/2072-666X/6/2/196 |
| work_keys_str_mv | AT shifeiliu adaptivecovarianceestimationmethodforlidaraidedmultisensorintegratednavigationsystems AT mohamedmaheratia adaptivecovarianceestimationmethodforlidaraidedmultisensorintegratednavigationsystems AT yanbingao adaptivecovarianceestimationmethodforlidaraidedmultisensorintegratednavigationsystems AT aboelmagdnoureldin adaptivecovarianceestimationmethodforlidaraidedmultisensorintegratednavigationsystems |