Maximum Likelihood Estimation of Monocular Optical Flow Field for Mobile Robot Ego-Motion

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 egomotion parameters closely follow the GPS/INS ground truth in complex outdoor road scenarios.

Online Offline Learning for Sound-Based Indoor Localization Using Low-Cost Hardware

“…The maximum likelihood method is used in regression analysis of linear transformation model and efficient and consistent estimator is developed for different data case of cohorts [13], [14]. MLE optical flow model in monocular optical flow field is developed and presented by authors in [15]. Domain adaptation using maximum likelihood linear transformation for speaker verification is presented by authors in [16], [17].…”