Abstract
This paper presents an optimized scheme of monocular ego-motion estimation to provide location and pose information for mobile robots with one fixed camera. First, a multi-scale hyper-complex wavelet phase-derived optical flow is applied to estimate micro motion of image blocks. Optical flow computation overcomes the difficulties of unreliable feature selection and feature matching of outdoor scenes; at the same time, the multi-scale strategy overcomes the problem of road surface self-similarity and local occlusions. Secondly, a support probability of flow vector is defined to evaluate the validity of the candidate image motions, and a Maximum Likelihood Estimation (MLE) optical flow model is constructed based not only on image motion residuals but also their distribution of inliers and outliers, together with their support probabilities, to evaluate a given transform. This yields an optimized estimation of inlier parts of optical flow. Thirdly, a sampling and consensus strategy is designed to estimate the ego-motion parameters. Our model and algorithms are tested on real datasets collected from an intelligent vehicle. The experimental results demonstrate the estimated ego-motion parameters closely follow the GPS/INS ground truth in complex outdoor road scenarios.
Highlights
Visual odometry involves the estimation of camera motion and the motion of the vehicle the camera is attached to, using a sequence of camera images
Hyper-complex Wavelet (HCW) is based on the definition of the 2-D Hilbert Transform (HT) and analytic signal [25] according to the theory of quaternion
The optical flow vector estimated in the HCW space on a larger scale only guides candidate image motions on a smaller scale
Summary
Visual odometry involves the estimation of camera motion and the motion of the vehicle the camera is attached to, using a sequence of camera images. Choi [20] presented a feature initialization and monocular EKF method for indoor-environment SLAM, and Milford and Wyeth [21] presented an appearance-based method to extract approximate rotational and translational velocity information from a single-perspective camera mounted on a car, which was used in a RatSLAM scheme. They used template tracking at the centre of the scene.
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