{"url":"/method/droid-slam","slug":"droid-slam","name":"DROID-SLAM","full_name":"DROID-SLAM","full_name_withheld":false,"description_markdown":"**DROID-SLAM** is a deep learning based SLAM system. It consists of recurrent iterative updates of camera pose and pixelwise depth through a Dense Bundle Adjustment layer. This layer leverages geometric constraints, improves accuracy and robustness, and enables a monocular system to handle stereo or RGB-D input without retraining. It builds a dense 3D map of the environment while simultaneously localizing the camera within the map.","description_state":"present","introduced_year":null,"introduced_by":{"title":null,"paper":null,"first_author":null,"n_authors":0,"url_abs":null,"archive_paper_url":null},"source":{"url":"https://arxiv.org/abs/2108.10869v2","title":"DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"General","area_id":"general","collection":"SLAM Methods","url":"/methods/category/slam-methods","pwc_aliases":[]}],"n_papers_tagged":6,"archive_num_papers":null,"papers_newest_first":[{"paper":null,"title":"SPAQ-DL-SLAM: Towards Optimizing Deep Learning-based SLAM for Resource-Constrained Embedded Platforms","date":"2024-09-22","arxiv_id":"2409.14515","n_code_links":0,"syntology":null},{"paper":"/paper/2408-01654","title":"Deep Patch Visual SLAM","date":"2024-08-03","arxiv_id":"2408.01654","n_code_links":1,"syntology":{"ran":5,"of":6,"unverified":1,"pointer_only":0}},{"paper":"/paper/volume-droid-a-real-time-implementation-of","title":"Volume-DROID: A Real-Time Implementation of Volumetric Mapping with DROID-SLAM","date":"2023-06-12","arxiv_id":"2306.06850","n_code_links":1,"syntology":null},{"paper":"/paper/deep-geometry-aware-camera-self-calibration","title":"Deep Geometry-Aware Camera Self-Calibration from Video","date":"2023-01-01","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/deflowslam-self-supervised-scene-motion","title":"D$^3$FlowSLAM: Self-Supervised Dynamic SLAM with Flow Motion Decomposition and DINO Guidance","date":"2022-07-18","arxiv_id":"2207.08794","n_code_links":1,"syntology":null},{"paper":"/paper/droid-slam-deep-visual-slam-for-monocular","title":"DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras","date":"2021-08-24","arxiv_id":"2108.10869","n_code_links":1,"syntology":{"ran":0,"of":3,"unverified":3,"pointer_only":0}}],"papers_shown":6,"tasks":[{"task":"/task/simultaneous-localization-and-mapping","name":"Simultaneous Localization and Mapping","papers":3},{"task":null,"name":"GPU","papers":1},{"task":"/task/point-cloud-generation","name":"Point Cloud Generation","papers":1},{"task":"/task/point-cloud-registration","name":"Point Cloud Registration","papers":1},{"task":"/task/pose-estimation","name":"Pose Estimation","papers":1},{"task":"/task/quantization","name":"Quantization","papers":1},{"task":"/task/semantic-segmentation","name":"Semantic Segmentation","papers":1},{"task":"/task/visual-odometry","name":"Visual Odometry","papers":1}],"tasks_shown":8,"n_tasks":8,"usage_by_year":[{"year":"2021","papers":1},{"year":"2022","papers":1},{"year":"2023","papers":2},{"year":"2024","papers":2}],"row_source":"embedded","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/droid-slam"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}