Poster
Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization
Lahav Lipson · Jia Deng
Arch 4A-E Poster #10
Abstract:
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. It 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 efficient global-optimization/loop-closure. Compared to other Multi-Session SLAM approaches, our design is more accurate and robust to catastrophic failures.
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