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"Time Delay Error Online Correction of LiDAR-IMU System Through MSCKF I" by Wanli Liu, Zhixiong Li et al.

When fusing the measurement data with different sampling frequencies from the light detection and ranging (LiDAR) and inertial measurement unit (IMU), their timestamps should be exactly aligned. However, in reality the timestamps of LiDAR and IMU are typically subject to different influences, which will inevitably generate the time delay error to reduce the accuracy and robustness of the LiDAR-IMU system. To this avail, this article proposes a new method that integrates the double layer recurrent neural network (DLRNN) and multistate constrained Kalman filter (MSCKF) to online correct the LiDAR-IMU time delay errors. In this new method, the MSCKF can improve the DLRNN training accuracy while in return the DLRNN can enhance the error estimating performance of the MSCKF. With this mutual improvement strategy, the time delay error can be precisely corrected in both the static and dynamic operation modes of the LiDAR-IMU system. The main contributions include: 1) Dual-information fusion is ....

Delay Effects , Laser Radar , Ight Detection And Ranging Inertial Measurement Unit Lidar Imu , Measurement Uncertainty , Online Correction , Q Measurement , Recurrent Neural Networks , Ime Delay Error ,

CTET 2022 application correction window to close today; know correction steps here

CTET 2022 application correction window to close today; know correction steps here
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Central Board , Secondary Education , Online Correction ,