Visual-LiDAR Odometry Aided by Reduced IMU
This paper proposes a method for combining stereo visual odometry, Light Detection And Ranging (LiDAR) odometry and reduced Inertial Measurement Unit (IMU) including two horizontal accelerometers and one vertical gyro. The proposed method starts with stereo visual odometry to estimate six Degree of...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-01-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | http://www.mdpi.com/2220-9964/5/1/3 |
| _version_ | 1818000878290862080 |
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| author | Yashar Balazadegan Sarvrood Siavash Hosseinyalamdary Yang Gao |
| author_facet | Yashar Balazadegan Sarvrood Siavash Hosseinyalamdary Yang Gao |
| author_sort | Yashar Balazadegan Sarvrood |
| collection | DOAJ |
| description | This paper proposes a method for combining stereo visual odometry, Light Detection And Ranging (LiDAR) odometry and reduced Inertial Measurement Unit (IMU) including two horizontal accelerometers and one vertical gyro. The proposed method starts with stereo visual odometry to estimate six Degree of Freedom (DoF) ego motion to register the point clouds from previous epoch to the current epoch. Then, Generalized Iterative Closest Point (GICP) algorithm refines the motion estimation. Afterwards, forward velocity and Azimuth obtained by visual-LiDAR odometer are integrated with reduced IMU outputs in an Extended Kalman Filter (EKF) to provide final navigation solution. In this paper, datasets from KITTI (Karlsruhe Institute of Technology and Toyota technological Institute) were used to compare stereo visual odometry, integrated stereo visual odometry and reduced IMU, stereo visual-LiDAR odometry and integrated stereo visual-LiDAR odometry and reduced IMU. Integrated stereo visual-LiDAR odometry and reduced IMU outperforms other methods in urban areas with buildings around. Moreover, this method outperforms simulated Reduced Inertial Sensor System (RISS), which uses simulated wheel odometer and reduced IMU. KITTI datasets do not include wheel odometry data. Integrated RTK (Real Time Kinematic) GPS (Global Positioning System) and IMU was replaced by wheel odometer to simulate the response of RISS method. Visual Odometry (VO)-LiDAR is not only more accurate than wheel odometer, but it also provides azimuth aiding to vertical gyro resulting in a more reliable and accurate system. To develop low-cost systems, it would be a good option to use two cameras plus reduced IMU. The cost of such a system will be reduced than using full tactical MEMS (Micro-Electro-Mechanical Sensor) based IMUs because two cameras are cheaper than full tactical MEMS based IMUs. The results indicate that integrated stereo visual-LiDAR odometry and reduced IMU can achieve accuracy at the level of state of art. |
| first_indexed | 2024-04-14T03:27:36Z |
| format | Article |
| id | doaj.art-b2e78fb6a41c45e28434e945d3d63880 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-04-14T03:27:36Z |
| publishDate | 2016-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-b2e78fb6a41c45e28434e945d3d638802022-12-22T02:15:07ZengMDPI AGISPRS International Journal of Geo-Information2220-99642016-01-0151310.3390/ijgi5010003ijgi5010003Visual-LiDAR Odometry Aided by Reduced IMUYashar Balazadegan Sarvrood0Siavash Hosseinyalamdary1Yang Gao2Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, CanadaCivil, Environmental and Geodetic Engineering Department, The Ohio State University, 2070 Neil Ave., Columbus, OH 43210, USADepartment of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr. NW, Calgary, AB T2N 1N4, CanadaThis paper proposes a method for combining stereo visual odometry, Light Detection And Ranging (LiDAR) odometry and reduced Inertial Measurement Unit (IMU) including two horizontal accelerometers and one vertical gyro. The proposed method starts with stereo visual odometry to estimate six Degree of Freedom (DoF) ego motion to register the point clouds from previous epoch to the current epoch. Then, Generalized Iterative Closest Point (GICP) algorithm refines the motion estimation. Afterwards, forward velocity and Azimuth obtained by visual-LiDAR odometer are integrated with reduced IMU outputs in an Extended Kalman Filter (EKF) to provide final navigation solution. In this paper, datasets from KITTI (Karlsruhe Institute of Technology and Toyota technological Institute) were used to compare stereo visual odometry, integrated stereo visual odometry and reduced IMU, stereo visual-LiDAR odometry and integrated stereo visual-LiDAR odometry and reduced IMU. Integrated stereo visual-LiDAR odometry and reduced IMU outperforms other methods in urban areas with buildings around. Moreover, this method outperforms simulated Reduced Inertial Sensor System (RISS), which uses simulated wheel odometer and reduced IMU. KITTI datasets do not include wheel odometry data. Integrated RTK (Real Time Kinematic) GPS (Global Positioning System) and IMU was replaced by wheel odometer to simulate the response of RISS method. Visual Odometry (VO)-LiDAR is not only more accurate than wheel odometer, but it also provides azimuth aiding to vertical gyro resulting in a more reliable and accurate system. To develop low-cost systems, it would be a good option to use two cameras plus reduced IMU. The cost of such a system will be reduced than using full tactical MEMS (Micro-Electro-Mechanical Sensor) based IMUs because two cameras are cheaper than full tactical MEMS based IMUs. The results indicate that integrated stereo visual-LiDAR odometry and reduced IMU can achieve accuracy at the level of state of art.http://www.mdpi.com/2220-9964/5/1/3stereo visual odometryLiDARreduced IMUGICPRISSintegrationEKF |
| spellingShingle | Yashar Balazadegan Sarvrood Siavash Hosseinyalamdary Yang Gao Visual-LiDAR Odometry Aided by Reduced IMU ISPRS International Journal of Geo-Information stereo visual odometry LiDAR reduced IMU GICP RISS integration EKF |
| title | Visual-LiDAR Odometry Aided by Reduced IMU |
| title_full | Visual-LiDAR Odometry Aided by Reduced IMU |
| title_fullStr | Visual-LiDAR Odometry Aided by Reduced IMU |
| title_full_unstemmed | Visual-LiDAR Odometry Aided by Reduced IMU |
| title_short | Visual-LiDAR Odometry Aided by Reduced IMU |
| title_sort | visual lidar odometry aided by reduced imu |
| topic | stereo visual odometry LiDAR reduced IMU GICP RISS integration EKF |
| url | http://www.mdpi.com/2220-9964/5/1/3 |
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