Dense RGB-D-inertial SLAM with map deformations
Open accessAbstract
While dense visual SLAM methods are capable of estimating dense reconstructions of the environment, they suffer from a lack of robustness in their tracking step, especially when the optimisation is poorly initialised. Sparse visual SLAM systems have attained high levels of accuracy and robustness through the inclusion of inertial measurements in a tightly-coupled fusion. Inspired by this performance, we propose the first tightly-coupled dense RGB-D-inertial SLAM system. Our system has real-time capability while running on a GPU. It jointly optimises for the camera pose, velocity, IMU biases and gravity direction while building up a globally consistent, fully dense surfel-based 3D reconstruction of the environment. Through a series of experiments on both synthetic and real world datasets, we show that our dense visual-inertial SLAM system is more robust to fast motions and periods of low texture and low geometric variation than a related RGB-D-only SLAM system.
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MSCKF has become a standard filtering-based approach to VIO.
“The multi-state constraint Kalman filter (MSCKF) [21] has become a standard for filtering-based approaches to VIO.”
From Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery (2020) · cites this paper as [21]
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Unsupervised Deep Visual-Inertial Odometry with Online Error Correction for RGB-D Imagery
Shamwell, E. J., Lindgren, K., Leung, S., & Nothwang, W. D. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2909895 · Cited by 86 · see citing works
- Authors
- Laidlow, T., Bloesch, M., Li, W., & Leutenegger, S.
- Affiliations
- Imperial College London (GB)
- Venue
- 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Publisher
- IEEE
- Published
- 2017-09
- Pages
- 6741-6748
- ISSN
- 2153-0858 (ISSN-L)
- OpenAlex
- W2772942222
- Cited by
- 61
- References
- 34
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 5 · 2023: 8 · 2022: 7 · 2021: 8 · 2020: 4 · 2019: 15 · 2018: 9 · 2017: 2
- FWCI
- 14.36
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Earth and Planetary Sciences › Geology › 3D Surveying and Cultural Heritage
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Keywords
- visual-inertial fusion, map deformation, camera pose estimation, IMU bias estimation, gravity direction estimation, real-time SLAM
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