Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization

Lahav Lipson, Jia Deng; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 19626-19635

We introduce a new system for Multi-Session SLAM which tracks camera motion across multiple disjoint videos under a single global reference. Our approach couples the prediction of optical flow with solver layers to estimate camera pose. The backbone is trained end-to-end using a novel differentiable solver for wide-baseline two-view pose. The full system can connect disjoint sequences perform visual odometry and global optimization. Compared to existing approaches our design is accurate and robust to catastrophic failures.