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- W3091486995 abstract "We present LINS, a lightweight lidar-inertial state estimator, for real-time ego-motion estimation. The proposed method enables robust and efficient navigation for ground vehicles in challenging environments, such as feature-less scenes, via fusing a 6-axis IMU and a 3D lidar in a tightly-coupled scheme. An iterated error-state Kalman filter (ESKF) is designed to correct the estimated state recursively by generating new feature correspondences in each iteration, and to keep the system computationally tractable. Moreover, we use a robocentric formulation that represents the state in a moving local frame in order to prevent filter divergence in a long run. To validate robustness and generalizability, extensive experiments are performed in various scenarios. Experimental results indicate that LINS offers comparable performance with the state-of-the-art lidar-inertial odometry in terms of stability and accuracy and has order-of-magnitude improvement in speed." @default.
- W3091486995 created "2020-10-08" @default.
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- W3091486995 date "2020-05-01" @default.
- W3091486995 modified "2023-10-18" @default.
- W3091486995 title "LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation" @default.
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- W3091486995 doi "https://doi.org/10.1109/icra40945.2020.9197567" @default.
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