Feature matching and tracking for visual odometry of mobile robot

A stereovision algorithm is proposed for visual odometry to estimate motion of mobile robot by providing feature pair sequence. It is composed of feature extracting, matching and tracking. Firstly, corners are extracted as features by Harris operator and grid-based optimizing. In feature matching and tracking, serious problems are caused by variable illumination between stereo images. An improved Moravec's Normalized Cross Correlation (MNCC) algorithm is presented to reduce illumination affect in computing correspondence of corners. On current stereo image pair, extracted corners are matched by correlation-based bidirectional algorithm and outliers are rejected by epipolar constraint. Matched corners are tracked in pre-estimated search windows. The computational cost is greatly reduced by limiting number of corners, pre-estimating search window and feature local-updating. Simulation results validate that our algorithm is efficient and reliable.