Deep-Init:Non Joint Initialization Method for Visual Inertial Odometry Based on Deep Learning
For a non-linear monocular VIO system,its initialization process is crucial,and the initialization result directly affects the accuracy of the state estimation during the whole system operation.To this end,this paper introduces a deep learning method into the initialization process of the monocular VIO system and proposes an efficient non-joint initialization method(referred to as Deep-Init).The core of this method is to use a deep neural network to accurately estimate the random error terms such as bias and noise of the gyroscope in the IMU,to obtain the key parameter in the initialization process,i.e.the bias of the gyroscope.At the same time,we loosely couple the IMU pre-integration to the SfM.The absolute scale,velocity and gravity vector are quickly recovered by position and rotation alignment using least squares,which are used as initial values to guide the non-linear tightly coupled optimization framework.The accuracy of the rotation estimates in the IMU is greatly increased due to the compensation of the gyroscope data by the deep neural network,which effectively improves the signal-to-noise ratio of the IMU data.This also reduces the number of least squares equation failures,further reducing the computational effort.Using the pre-integrated amount of gyroscope data with the error term removed to replace the rotation amount in the SfM and using the IMU rotation amount as the true value,not only avoids the errors associated with initializing inaccurate SfM values as the true value but slao effectively improves the accuracy of system state estimation.Moreover,it enables effective adaptation to scenarios where SfM estimation is poor,such as high-speed motion,drastic lighting changes and texture repetition.The validity of the proposed method is verified on the EuRoC dataset,and the experimental results show that the proposed Deep-Init initialization method achieves good results in terms of both accuracy and time consumption.
Visual-inertial odometryDeep learningInitializationInertial measurement unit
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