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Sensors 2016, 16(10), 1704;

A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera

School of Optical-Electrical and Computer Engineering, University of Shanghai for Science & Technology, Shanghai 200093, China
School of Electric Power Engineering, Nanjing Normal University Taizhou Colledge, Taizhou 225300, China
Author to whom correspondence should be addressed.
Academic Editor: Felipe Jimenez
Received: 12 July 2016 / Revised: 8 September 2016 / Accepted: 30 September 2016 / Published: 17 October 2016
(This article belongs to the Special Issue Sensors for Autonomous Road Vehicles)
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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. View Full-Text
Keywords: visual odometry; ego-motion; stereovision; optical flow; RANSAC algorithm; space position constraint visual odometry; ego-motion; stereovision; optical flow; RANSAC algorithm; space position constraint

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Ci, W.; Huang, Y. A Robust Method for Ego-Motion Estimation in Urban Environment Using Stereo Camera. Sensors 2016, 16, 1704.

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