Self-Supervised Deep Visual Odometry Based on Geometric Attention Model

Existing learning-based algorithms have a certain potential in visual odometry. In this work, we propose the solution of the learning-based method, which contains the attention mechanism and pose graph optimization. We set a self-supervised network as our backbone to cope with image data and error-heavy estimation pose for pose correction. The pre-processing camera poses involved in the network can provide prior information. Combining the advantages of the abundant feature information and efficient attention mechanism, we design a geometric attention module that is sensitive to geometrical structure from images to accurately regress the rotation matrix. Then we improve the loss function with the weights of the attention module to consider the diversity of the data. Experimental results demonstrate the effectiveness and reliability of our approach on the public datasets KITTI with monocular task and stereo task. In comparison, the proposed method is superior to the existing methods in the translation component. In the self-supervised network, learning an attention mechanism can extract an effective connect relation of feature maps. We conduct ablation experiments under the self-supervised network backbone setting different strategies, and conclude that the proposed attention module is applicable to various sequences, and provide loss function improvements on the visual odometry task.