Learning Generalized Visual Odometry Using Position-Aware Optical Flow and Geometric Bundle Adjustment
This work addresses the problem of robust visual odometry for autonomous systems in varied and noisy conditions, representing an incremental improvement over existing hybrid methods.
The paper tackles the challenge of improving generalization in visual odometry under noisy environments and diverse scenes by proposing a novel optical flow network (PANet) and enhanced bundle adjustment, achieving comparable performance on KITTI and significant generalization gains on noisy KITTI and KAIST datasets.
Recent visual odometry (VO) methods incorporating geometric algorithm into deep-learning architecture have shown outstanding performance on the challenging monocular VO task. Despite encouraging results are shown, previous methods ignore the requirement of generalization capability under noisy environment and various scenes. To address this challenging issue, this work first proposes a novel optical flow network (PANet). Compared with previous methods that predict optical flow as a direct regression task, our PANet computes optical flow by predicting it into the discrete position space with optical flow probability volume, and then converting it to optical flow. Next, we improve the bundle adjustment module to fit the self-supervised training pipeline by introducing multiple sampling, ego-motion initialization, dynamic damping factor adjustment, and Jacobi matrix weighting. In addition, a novel normalized photometric loss function is advanced to improve the depth estimation accuracy. The experiments show that the proposed system not only achieves comparable performance with other state-of-the-art self-supervised learning-based methods on the KITTI dataset, but also significantly improves the generalization capability compared with geometry-based, learning-based and hybrid VO systems on the noisy KITTI and the challenging outdoor (KAIST) scenes.