Depth Prediction for Monocular Direct Visual Odometry

Depth prediction from monocular images with deep CNNs is a topic of increasing interest to the community. Advances have lead to models capable of predicting disparity maps with consistent scale, which are an acceptable prior for gradient-based direct methods. With this in consideration, we exploit depth prediction as a candidate prior for the coarse initialization, tracking, and marginalization steps of the direct visual odometry system, enabling the second-order optimizer to converge faster into a precise global minimum. In addition, the given depth prior supports large baseline stereo scenarios, maintaining robust pose estimations against challenging motion states such as in-place rotation. We further refine our pose estimation with semi-online loop closure. The experiments on KITTI demonstrate that our proposed method achieves state- of-the-art performance compared to both traditional direct visual odometry and learning-based counterparts.