A two-stage sampling for robust feature matching

For any visual feature based SLAM solutions, to estimate the relative camera motion between two images, it is necessary to find “correct” correspondence between features extracted from those images. Given a set of feature correspondents, one can use a n-point algorithm with robust estimation method, to produce the best estimate to the relative camera pose. The accuracy of a motion estimate is heavily dependent upon the accuracy of the feature correspondence. Such a dependency is even more significant when features are extracted from the images of the scenes with drastic changes in viewpoints and illuminations, and presence of occlusions. To make a feature matching robust to such challenging scenes, we propose a new feature matching method that incrementally chooses a five pairs of matched features for a full DoF camera motion estimation. In particular, at the first stage, we use our 2-point algorithm to estimate a camera motion, and at the second stage, use this estimated motion to choose three more matched features. In addition, we use, instead of the epipolar constraint, a planar constraint for more accurate outlier rejection. With this set of five matching features, we estimate a full DoF (Degree of Freedom) camera motion with scale ambiguity. Through the experiments with three, real-world datasets, our method demonstrates its effectiveness and robustness by successfully matching features 1) from the images of a night market where presenceof frequent occlusions and varying illuminations, 2) from the images of a night market taken by a handheld camera and by the Google street view, and 3) from the images of a same location taken daytime and nighttime.

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Chih-Chung Chou, YoungWoo Seo, and Chieh-Chih Wang, A two-stage sampling for robust feature matching, Journal of Field Robotics, 35(5): 779-801, 2018.