Local Pose optimization with an Attentionbased Neural Network In this paper, we propose a novel pose optimizer which can be inserted into either supervised or unsupervised endtoend 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 attentionbased 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.

On the Tunable Sparse Graph Solver for Pose Graph Optimization in Visual SLAM Problems We report a tunable sparse optimization solver that can trade a slight decrease in accuracy for significant speed improvement in pose graph optimization in visual simultaneous localization and mapping (vSLAM). The solver is designed for devices with significant computation and power constraints such as mobile phones or tablets. Two approaches have been combined in our design. The first is a graph pruning strategy by exploiting objective function structure to reduce the optimization problem size which further sparsifies the optimization problem. The second step is to accelerate each optimization iteration in solving increments for the gradientbased search in GaussNewton type optimization solver. We apply a modified Cholesky factorization and reuse the decomposition result from last iteration by using Cholesky updatedowndate to accelerate the computation. We have implemented our solver and tested it with open source data. The experimental results show that our solver can be twice as fast as the counterpart while maintaining a loss of less than 5% in accuracy.