Snake-SLAM: Efficient Global Visual Inertial SLAM using Decoupled Nonlinear Optimization
Rückert D, Stamminger M (2021)
Publication Language: English
Publication Type: Conference contribution, Original article
Publication year: 2021
Event location: Athen
URI: https://ieeexplore.ieee.org/abstract/document/9476760
DOI: 10.1109/icuas51884.2021.9476760
Abstract
Snake-SLAM is a scalable visual inertial SLAM system for autonomous navigation in low-power aerial devices. The tracking front-end features map reuse, loop closing, relocalization, and supports monocular, stereo, and RGBD input. The keyframes are reduced by a graph-based simplification approach and further refined using a novel deferred mapping stage to ensure a sparse yet accurate global map. The optimization back-end decouples IMU state estimation from visual bundle adjustment and solves them separately in two simplified sub problems. This greatly reduces computational complexity and allows Snake-SLAM to use a larger local window size than existing SLAM methods. Our system implements a novel multistage VI initialization scheme, which uses gyroscope data to detect visual outliers and recovers metric velocity, gravity, and scale. We evaluate Snake-SLAM on the EuRoC dataset and show that it outperforms all other approaches in efficiency while also achieving state-of-the-art tracking accuracy.
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How to cite
APA:
Rückert, D., & Stamminger, M. (2021). Snake-SLAM: Efficient Global Visual Inertial SLAM using Decoupled Nonlinear Optimization. In Proceedings of the 2021 International Conference on Unmanned Aircraft Systems (ICUAS). Athen, GR.
MLA:
Rückert, Darius, and Marc Stamminger. "Snake-SLAM: Efficient Global Visual Inertial SLAM using Decoupled Nonlinear Optimization." Proceedings of the 2021 International Conference on Unmanned Aircraft Systems (ICUAS), Athen 2021.
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