Online Initialization and Extrinsic Spatial-Temporal Calibration for Monocular Visual-Inertial Odometry

• URL: http://arxiv.org/abs/2004.05534v1
• Date: Sun, 12 Apr 2020 03:13:08 GMT
• Title: Online Initialization and Extrinsic Spatial-Temporal Calibration for Monocular Visual-Inertial Odometry
• Authors: Weibo Huang, Hong Liu, Weiwei Wan
• Abstract summary: This paper presents an online method for bootstrapping the optimization-based monocular visual-inertial odometry (VIO) The method can online calibrate the relative transformation (spatial) and time offsets (temporal) among camera and IMU, as well as estimate the initial values of metric scale, velocity, gravity, gyroscope bias, and accelerometer bias. Experimental results on public datasets show that the initial values and the parameters, as well as the sensor poses, can be accurately estimated by the proposed method.
• Score: 19.955414423860788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper presents an online initialization method for bootstrapping the optimization-based monocular visual-inertial odometry (VIO). The method can online calibrate the relative transformation (spatial) and time offsets (temporal) among camera and IMU, as well as estimate the initial values of metric scale, velocity, gravity, gyroscope bias, and accelerometer bias during the initialization stage. To compensate for the impact of time offset, our method includes two short-term motion interpolation algorithms for the camera and IMU pose estimation. Besides, it includes a three-step process to incrementally estimate the parameters from coarse to fine. First, the extrinsic rotation, gyroscope bias, and time offset are estimated by minimizing the rotation difference between the camera and IMU. Second, the metric scale, gravity, and extrinsic translation are approximately estimated by using the compensated camera poses and ignoring the accelerometer bias. Third, these values are refined by taking into account the accelerometer bias and the gravitational magnitude. For further optimizing the system states, a nonlinear optimization algorithm, which considers the time offset, is introduced for global and local optimization. Experimental results on public datasets show that the initial values and the extrinsic parameters, as well as the sensor poses, can be accurately estimated by the proposed method.

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