Local Pose optimization with an Attention-based Neural Network

In this paper, we propose a novel pose optimizer which can be inserted into either supervised or unsupervised end-to-end visual odometry for the purpose of local pose optimization. The pose optimizer is an analogue of the pose graph optimization used in traditional VSLAM algorithms. Local pose optimization is performed by an attention-based neural network which iteratively refines the predicted pose estimates of an image snippet. Instead of complicated graph convolutional network, the attention mechanism based on geometric consistency of trajectory constraint is utilized because pose features whose spatial distribution is not important can be flattened to vectors and then processed. The pose optimizer is aimed at improving pose estimation accuracy by redistributing errors of pose estimates. Quantitative and qualitative evaluation of the proposed approach on the KITTI Odometry dataset [1] is presented to demonstrate its effectiveness in improving pose estimation accuracy and minimizing pose drift.