Reliable scale estimation and correction for monocular Visual Odometry

Recovering absolute scale (i.e. metric information) from monocular vision system is a very challenging problem yet is highly desirable for vision-based autonomous driving. This paper proposes a new method for scale recovery, based on the idea of knowing camera height (relative to ground-plane). While this idea of using known camera height is not new in this context, existing implementations of this idea suffer significantly from severe numerical instability arisen in the ground plane homography decomposition stage. Our novel contribution of this work is to alleviate this issue by a divide and conquer approach, i.e. decomposing the motion parameters in the homography from the structure parameters of the ground plane. We also describe a robust procedure to correct scale drift in the monocular visual odometry system. Experimental results on KITTI standard benchmark dataset [1] and our self-collected driving dataset both show significant improvements.