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Online self-supervised monocular visual odometry for ground vehicles

This paper presents an online self-supervised approach to monocular visual odometry and ground classification applied to ground vehicles. We solve the motion and structure problem based on a constrained kinematic model. The true scale of the monocular scene is recovered by estimating the ground surface. We consider a general parametric ground surface model and use the Random Sample Consensus (RANSAC) algorithm for robust fitting of the parameters. The estimated ground surface provides training samples to learn a probabilistic appearance-based ground classifier in an online and self-supervised manner. The appearance-based classifier is then used to bias the RANSAC sampling to generate better hypotheses for parameter estimation of the ground surface model. Thus, without relying on any prior information, we combine geometric estimates with appearance-based classification to achieve an online self-learning scheme from monocular vision. Experimental results demonstrate that online learning improves the computational efficiency and accuracy compared to standard sampling in RANSAC. Evaluations on the KITTI benchmark dataset demonstrate the stability and accuracy of our overall methods in comparison to previous approaches.