Summary of Bodyslam: a Generalized Monocular Visual Slam Framework For Surgical Applications, by G. Manni (1 and 2) et al.
BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical Applications
by G. Manni, C. Lauretti, F. Prata, R. Papalia, L. Zollo, P. Soda
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this study, researchers propose BodySLAM, a deep learning-based approach to Monocular Visual Simultaneous Localization and Mapping (MVSLAM) specifically designed for endoscopic surgery. The model addresses challenges posed by hardware limitations in existing MVSLAM systems, including the use of monocular cameras and lack of odometry sensors. BodySLAM comprises three key components: CycleVO, a novel pose estimation module; Zoe architecture for monocular depth estimation; and a 3D reconstruction module creating a coherent surgical map. The approach is evaluated using three publicly available datasets (Hamlyn, EndoSLAM, and SCARED) spanning laparoscopy, gastroscopy, and colonoscopy scenarios, and benchmarked against four state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Endoscopic surgery relies on 2D views, making it difficult for surgeons to perceive depth and manipulate instruments. Researchers are working on Monocular Visual Simultaneous Localization and Mapping (MVSLAM) to help solve this problem. This study introduces BodySLAM, a new approach that uses deep learning to improve MVSLAM in endoscopic surgery. It’s designed to overcome hardware limitations like using only one camera and no odometry sensors. The team tested BodySLAM on three datasets covering different types of endoscopic procedures and compared it to other methods. |
Keywords
* Artificial intelligence * Deep learning * Depth estimation * Pose estimation
