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- W4376603950 abstract "Visual-inertial navigation system (VINS) is subject to degradation in urban vehicles. Specific motion patterns of the car and dynamic objects considerably reduce the pose estimation accuracy of VINS. A tightly-coupled visual-inertial-wheel odometry based on factor graph optimization with sliding window for ground vehicles is proposed in this paper. Wheel encoder and non-holonomic constraint information are utilized in pre-integration as an additional constraint to assist scale estimation. To promote the robustness of the system in complex environments, the prior pose from dead reckoning is employed to assist feature tracking and dynamic object filtering. The results from the real-world experiments in large-scale urban driving environments show better robustness and accuracy compared to the state-of-the-art methods." @default.
- W4376603950 created "2023-05-17" @default.
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- W4376603950 date "2023-05-15" @default.
- W4376603950 modified "2023-10-14" @default.
- W4376603950 title "Monocular visual-inertial odometry with wheel encoder for urban vehicle" @default.
- W4376603950 doi "https://doi.org/10.1117/12.2679092" @default.
- W4376603950 hasPublicationYear "2023" @default.
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