@inproceedings{64d0e61b98234637a0727f066472b783,
title = "Multi-Session SLAM with Differentiable Wide-Baseline Pose Optimization",
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. 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 visualodometry, and global optimization. Compared to existing approaches, our design is accurate and robust to catas-trophic failures. Code is available at https://github.com/princeton-v1/MultiSlam-DiffPose",
keywords = "Camera Pose, Odometry, SLAM, Tracking",
author = "Lahav Lipson and Jia Deng",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 ; Conference date: 16-06-2024 Through 22-06-2024",
year = "2024",
doi = "10.1109/CVPR52733.2024.01856",
language = "English (US)",
series = "Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition",
publisher = "IEEE Computer Society",
pages = "19626--19635",
booktitle = "Proceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024",
address = "United States",
}