# CudaSIFT-SLAM: multiple-map visual SLAM for full procedure mapping in real human endoscopy

arXiv: https://arxiv.org/abs/2405.16932 · HTML: https://mlsanity.com/paper/2405.16932/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 9 of 9)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [Colonoscopy 3D Video Dataset with Paired Depth from 2D-3D Registration](https://mlsanity.com/paper/2206.08903/) (2022)
- [EndoMapper dataset of complete calibrated endoscopy procedures](https://mlsanity.com/paper/2204.14240/) (2022)
- [MIS-SLAM: Real-time Large Scale Dense Deformable SLAM System in Minimal Invasive Surgery Based on Heterogeneous Computing](https://mlsanity.com/paper/1803.02009/) (2018)
- [SAGE: SLAM with Appearance and Geometry Prior for Endoscopy](https://mlsanity.com/paper/2202.09487/) (2022)
- [NR-SLAM: Non-Rigid Monocular SLAM](https://mlsanity.com/paper/2308.04036/) (2023)
- [BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgery](https://mlsanity.com/paper/2312.15679/) (2023)

## Built on by (most cited first, 11 of 11)

- [C3VDv2 -- Colonoscopy 3D video dataset with enhanced realism](https://mlsanity.com/paper/2506.24074/) (2025)
- [Topological SLAM in colonoscopies leveraging deep features and topological priors](https://mlsanity.com/paper/2409.16806/) (2024)
- [Image Matching Filtering and Refinement by Planes and Beyond](https://mlsanity.com/paper/2411.09484/) (2024)
- [EndoMetric: Near-Light Monocular Metric Scale Estimation in Endoscopy](https://mlsanity.com/paper/2410.15065/) (2024)
- [3D Densification for Multi-Map Monocular VSLAM in Endoscopy](https://mlsanity.com/paper/2503.14346/) (2025)
- [Endoscopic Depth Estimation Based on Deep Learning: A Survey](https://mlsanity.com/paper/2507.20881/) (2025)
- [Investigating a Policy-Based Formulation for Endoscopic Camera Pose Recovery](https://mlsanity.com/paper/2603.20045/) (2026)
- [Expert Consensus on Criteria for the Automated Assessment of Laparoscopic Camera Navigation](https://mlsanity.com/paper/2606.23131/) (2026)
- [EndoMD-SLAM: Endoscopic Gaussian Splatting SLAM under Optical Degradation with Memory and Static-Transient Decomposition](https://mlsanity.com/paper/2608.08949/) (2026)
- [Lesion-centered 3D mapping of colonoscopy procedures: validation of a hierarchical ensemble pipeline on public benchmark videos](https://mlsanity.com/paper/2609.16672/) (2026)
- [LapaTrack-3D: 6 DoF pre-operative shape tracking for laparoscopic surgery](https://mlsanity.com/paper/2609.19954/) (2026)

## Abstract (arXiv)

Monocular visual simultaneous localization and mapping (V-SLAM) is nowadays an irreplaceable tool in mobile robotics and augmented reality, where it performs robustly. However, human colonoscopies pose formidable challenges like occlusions, blur, light changes, lack of texture, deformation, water jets or tool interaction, which result in very frequent tracking losses. ORB-SLAM3, the top performing multiple-map V-SLAM, is unable to recover from them by merging sub-maps or relocalizing the camera, due to the poor performance of its place recognition algorithm based on ORB features and DBoW2 bag-of-words. We present CudaSIFT-SLAM, the first V-SLAM system able to process complete human colonoscopies in real-time. To overcome the limitations of ORB-SLAM3, we use SIFT instead of ORB features and replace the DBoW2 direct index with the more computationally demanding brute-force matching, being able to successfully match images separated in time for relocation and map merging. Real-time performance is achieved thanks to CudaSIFT, a GPU implementation for SIFT extraction and brute-force matching. We benchmark our system in the C3VD phantom colon dataset, and in a full real colonoscopy from the Endomapper dataset, demonstrating the capabilities to merge sub-maps and relocate in them, obtaining significantly longer sub-maps. Our system successfully maps in real-time 88 % of the frames in the C3VD dataset. In a real screening colonoscopy, despite the much higher prevalence of occluded and blurred frames, the mapping coverage is 53 % in carefully explored areas and 38 % in the full sequence, a 70 % improvement over ORB-SLAM3.