  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. Code is available at github.com/princeton-vl/MultiSlam_DiffPose
