Paper WeB1.2
Rückert, Darius (University of Erlangen-Nuremberg), Stamminger, Marc (University of Erlangen-Nuremberg)
Snake-SLAM: Efficient Global Visual Inertial SLAM Using Decoupled Nonlinear Optimization
Scheduled for presentation during the Regular Session "Navigation" (WeB1), Wednesday, June 16, 2021,
14:20−14:40, Macedonia Hall
2021 International Conference on Unmanned Aircraft Systems (ICUAS), June 15-18, 2021, Athens, Greece
This information is tentative and subject to change. Compiled on March 29, 2024
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Keywords Navigation, Sensor Fusion, Energy Efficient UAS
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 multi-stage 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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