A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera
Visual odometry estimates the ego-motion of an agent (e.g., vehicle and robot) using image information and is a key component for autonomous vehicles and robotics. This paper proposes a robust and precise method for estimating the 6-DoF ego-motion, using a stereo rig with optical flow analysis. An o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-10-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/10/1704 |
| _version_ | 1798034933004894208 |
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| author | Wenyan Ci Yingping Huang |
| author_facet | Wenyan Ci Yingping Huang |
| author_sort | Wenyan Ci |
| collection | DOAJ |
| description | Visual odometry estimates the ego-motion of an agent (e.g., vehicle and robot) using image information and is a key component for autonomous vehicles and robotics. This paper proposes a robust and precise method for estimating the 6-DoF ego-motion, using a stereo rig with optical flow analysis. An objective function fitted with a set of feature points is created by establishing the mathematical relationship between optical flow, depth and camera ego-motion parameters through the camera’s 3-dimensional motion and planar imaging model. Accordingly, the six motion parameters are computed by minimizing the objective function, using the iterative Levenberg–Marquard method. One of key points for visual odometry is that the feature points selected for the computation should contain inliers as much as possible. In this work, the feature points and their optical flows are initially detected by using the Kanade–Lucas–Tomasi (KLT) algorithm. A circle matching is followed to remove the outliers caused by the mismatching of the KLT algorithm. A space position constraint is imposed to filter out the moving points from the point set detected by the KLT algorithm. The Random Sample Consensus (RANSAC) algorithm is employed to further refine the feature point set, i.e., to eliminate the effects of outliers. The remaining points are tracked to estimate the ego-motion parameters in the subsequent frames. The approach presented here is tested on real traffic videos and the results prove the robustness and precision of the method. |
| first_indexed | 2024-04-11T20:51:15Z |
| format | Article |
| id | doaj.art-aa00cd6a178440a889f99006ed66b242 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T20:51:15Z |
| publishDate | 2016-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-aa00cd6a178440a889f99006ed66b2422022-12-22T04:03:50ZengMDPI AGSensors1424-82202016-10-011610170410.3390/s16101704s16101704A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo CameraWenyan Ci0Yingping Huang1School of Optical-Electrical and Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, ChinaSchool of Optical-Electrical and Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, ChinaVisual odometry estimates the ego-motion of an agent (e.g., vehicle and robot) using image information and is a key component for autonomous vehicles and robotics. This paper proposes a robust and precise method for estimating the 6-DoF ego-motion, using a stereo rig with optical flow analysis. An objective function fitted with a set of feature points is created by establishing the mathematical relationship between optical flow, depth and camera ego-motion parameters through the camera’s 3-dimensional motion and planar imaging model. Accordingly, the six motion parameters are computed by minimizing the objective function, using the iterative Levenberg–Marquard method. One of key points for visual odometry is that the feature points selected for the computation should contain inliers as much as possible. In this work, the feature points and their optical flows are initially detected by using the Kanade–Lucas–Tomasi (KLT) algorithm. A circle matching is followed to remove the outliers caused by the mismatching of the KLT algorithm. A space position constraint is imposed to filter out the moving points from the point set detected by the KLT algorithm. The Random Sample Consensus (RANSAC) algorithm is employed to further refine the feature point set, i.e., to eliminate the effects of outliers. The remaining points are tracked to estimate the ego-motion parameters in the subsequent frames. The approach presented here is tested on real traffic videos and the results prove the robustness and precision of the method.http://www.mdpi.com/1424-8220/16/10/1704visual odometryego-motionstereovisionoptical flowRANSAC algorithmspace position constraint |
| spellingShingle | Wenyan Ci Yingping Huang A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera Sensors visual odometry ego-motion stereovision optical flow RANSAC algorithm space position constraint |
| title | A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera |
| title_full | A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera |
| title_fullStr | A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera |
| title_full_unstemmed | A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera |
| title_short | A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera |
| title_sort | robust method for ego motion estimation in urban environment using stereo camera |
| topic | visual odometry ego-motion stereovision optical flow RANSAC algorithm space position constraint |
| url | http://www.mdpi.com/1424-8220/16/10/1704 |
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