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DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras

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arxiv 2108.10869 v2 pith:CZEDW4W2 submitted 2021-08-24 cs.CV

Zachary Teed , Jia Deng This is my paper · ORCID
classification cs.CV
keywords droid-slamdeepmonocularrgb-dslamstereovideoaccurate
verification ladder T0 review T1 audit T2 compute T3 formal
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We introduce DROID-SLAM, a new deep learning based SLAM system. DROID-SLAM consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense Bundle Adjustment layer. DROID-SLAM is accurate, achieving large improvements over prior work, and robust, suffering from substantially fewer catastrophic failures. Despite training on monocular video, it can leverage stereo or RGB-D video to achieve improved performance at test time. The URL to our open source code is https://github.com/princeton-vl/DROID-SLAM.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Princeton365: A Diverse Dataset with Accurate Camera Pose

    cs.CV 2025-06 conditional novelty 7.0 of 10

    Princeton365 is a 365-video SLAM/NVS benchmark with board-calibrated millimeter-accurate 6-DoF poses, a new scale-aware optical-flow error metric, and an NVS benchmark of fully non-Lambertian 360-degree scans.

  2. Rig3R: Rig-Aware Conditioning for Learned 3D Reconstruction

    cs.CV 2025-06 conditional novelty 7.0 of 10

    Rig3R conditions learned 3D reconstruction on optional rig metadata and predicts rig-relative raymaps, enabling state-of-the-art pose estimation and rig calibration discovery from images.

  3. NSL-SLAM: High-Fidelity Neural Structured-Light Depth for Practical SLAM and Reconstruction

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Injecting frozen monocular depth features into neural structured-light decoding cuts Replica-SL depth RMSE ~35% vs NSL and, with depth-centric GICP+sparse anchors+light BA, yields the most stable real D435 SLAM among ...

  4. Enhancing Situational Awareness in Underwater Robotics with Multi-modal Spatial Perception

    cs.RO 2025-06 conditional novelty 4.0 of 10

    The authors present new ROV field datasets and qualitative demonstrations of multi-camera, DROID-SLAM, and semantic projection in underwater conditions, without quantitative validation.

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