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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 ....

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Baden Wüberg , James Hendler , Bill Gates , Sebastien Bubeck , Colin Raffel , Sandrine Ceurstemont , Roman Trukhin , University Of North Carolina At Chapel Hill , Machine Learning Foundations Group At Microsoft Research , Interest Group On Artificial Intelligence , Cognitive Sciences At Rensselaer Polytechnic Institute , Heidelberg Laureate Forum , Recurrent Neural Networks Rnns , Machine Learning Foundations Group , Recurrent Neural Networks , Tetherless World Professor , Cognitive Sciences , Rensselaer Polytechnic Institute , Interest Group , Artificial Intelligence , North Carolina , Chapel Hill ,

Uncovering Hidden Patterns: Time Series Analysis Techniques

Time series data, which comprises observations collected over sequential time intervals, is ubiquitous in various fields like finance, economics, climate ....

Recurrent Neural Networks Rnns , Time Series , Defining Time Series , Time Series Data , Moving Averages , Exponential Smoothing , Decomposition Components , Integrated Moving Average , Parameter Selection , Frequency Domain Analysis , Spectral Analysis , Supervised Learning Approaches , Recurrent Neural Networks , Statistical Methods , Machine Learning Based Approaches , Isolation Forest , Performance Metrics , Mean Absolute Error , Root Mean Squared Error , Cross Validation Techniques ,

New Method for More Accurate Wind Speed Forecasting

Wind-generated electricity is poised to assume a pivotal role in the global energy landscape as the world shifts away from fossil fuels towards renewable sources. ....

Fuzhan Nasiri , Anton Kaifel , Skyla Baily , Pilar Monsalvete , Ramanunni Menon , Ursula Eicker , Matt Artz , Canada Excellence Research , Concordia University Next Generation Cities Institute , Vehicle Battery Technology Industry , Agency Institutional Program Secretariat , Concordia University Montreal , Solar Energy , International Energy Agency , Department Of Building , Recurrent Neural Networks , Research Scientist , Gina Cody School Of Engineering , Vehicle Battery Technology , Concordia University , Hydrogen Research In Stuttgart , More Accurate Wind Speed Forecasting , Image Credit , Environmental Engineering , Gina Cody School , Computer Science ,

Theoretical Introduction to Recurrent Neural Networks: A Key Architecture for Time Series Data…

Are you interested in mastering Time Series forecasting or natural language processing? Then you should learn about Recurrent Neural Networks. Recurrent… ....

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