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Vision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online Error Correction
[article]
2018
arXiv
pre-print
We present an unsupervised deep neural network approach to the fusion of RGB-D imagery with inertial measurements for absolute trajectory estimation. Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns to perform visual-inertial odometry (VIO) without inertial measurement unit (IMU) intrinsic parameters (corresponding to gyroscope and accelerometer bias or white noise) or the extrinsic calibration between an IMU and camera. The network learns to integrate IMU
arXiv:1803.05850v1
fatcat:ypambrewvbca5nq4lyyhrg7ete