摘要：In this paper, we present a visual-inertial odometry (VIO) with an online calibration using a stereo camera in planetary rover localization. We augment the state vector with extrinsic (rigid body transformation) and temporal (time-offset) parameters of a camera-IMU system in a framework of an extended Kalman filter. This is motivated by the fact that when fusing independent systems, it is practically crucial to obtain precise extrinsic and temporal parameters. Unlike the conventional calibration procedures, this method estimates both navigation and calibration states from naturally occurred visual point features during operation. We describe mathematical formulations of the proposed method, and it is evaluated through the author-collected dataset which is recorded by the commercially available visual-inertial sensor installed on the testing rover in the environment lack of vegetation and artificial objects. Our experimental results showed that 3D return position error as 1.54m of total 173m traveled and 10ms of time-offset with the online calibration, while 6.52m of return position error without the online calibration.
其他摘要：In this paper, we present a visual-inertial odometry (VIO) with an online calibration using a stereo camera in planetary rover localization. We augment the state vector with extrinsic (rigid body transformation) and temporal (time-offset) parameters of a camera-IMU system in a framework of an extended Kalman filter. This is motivated by the fact that when fusing independent systems, it is practically crucial to obtain precise extrinsic and temporal parameters. Unlike the conventional calibration procedures, this method estimates both navigation and calibration states from naturally occurred visual point features during operation. We describe mathematical formulations of the proposed method, and it is evaluated through the author-collected dataset which is recorded by the commercially available visual-inertial sensor installed on the testing rover in the environment lack of vegetation and artificial objects. Our experimental results showed that 3D return position error as 1.54m of total 173m traveled and 10ms of time-offset with the online calibration, while 6.52m of return position error without the online calibration.
