Inertial measurement unit-camera calibration based on incomplete inertial sensor information

Abstract

This paper is concerned with the problem of estimating the relative orientation between an inertial measurement unit (IMU) and a camera. Unlike most existing IMU-camera calibrations, the main challenge in this paper is that the information output from the IMU is incomplete. For example, only two tilt information can be read from the gravity sensor of a smart phone. Despite incomplete inertial information, there are strong restrictions between the IMU and camera coordinate systems. This paper addresses the incomplete information based IMUcamera calibration problem by exploiting the intrinsic restrictions among the coordinate transformations. First, the IMU transformation between two poses is formulated with the unknown IMU information. Then the defective IMU information is restored using the complementary visual information. Finally, the Levenberg-Marquardt (LM) algorithm is applied to estimate the optimal calibration result in noisy environments. Experiments on both synthetic and real data show the validity and robustness of our algorithm.

Combined denoising method of acceleration signal during mobile robot collision

“…Compared with the collision force measurement by collision plate detector, the accelerometer which installed on the robot can quickly feedback the motion state of the robot in the whole collision process [5,6]. However, the accelerometer measurement results are easily affected by the vibration, impact and other signals generated by the robot movement process.…”

Properties of a general quaternion-valued gradient operator and its applications to signal processing

“…Quaternion calculus has been introduced in signal processing with application areas involving threeor four-dimensional signals, such as color image processing (Pei and Cheng, 1999;Sangwine and Ell, 2000;Parfieniuk and Petrovsky, 2010;Ell et al, 2014;Liu et al, 2014), vector-sensor array systems (Le Bihan and Mars, 2004;Miron et al, 2006;Le Bihan et al, 2007;Tao, 2013;Tao and Chang, 2014;Zhang et al, 2014;Hawes and Liu, 2015), three-phase power ‡ Corresponding author * Project supported by the National Grid UK # Part of the work is available at http://arxiv.org/abs/1407.5178 (Jiang et al, 2014a) ORCID: Wei LIU, http://orcid.org/0000-0003-2968-2888…”

Filtering and tracking with trinion-valued adaptive algorithms

“…Multidimensional (m-D) signal processing has a variety of applications and the modeling of multiple variables is carried out traditionally within the real-valued matrix algebra, while in recent years we have observed the successful exploitation of hypercomplex numbers in areas including colour image processing (Pei and Cheng, 1999;Pei et al, 2004;Sangwine and Ell, 2000;Parfieniuk and Petrovsky, 2010;Ell et al, 2014;Liu et al, 2014), vector-sensor array processing (Le Bihan and Mars, 2004;Miron et al, 2006;Le Bihan et al, 2007;Tao, 2013;Tao and Chang, 2014;Zhang et al, 2014;Hawes and Liu, 2015;Jiang et al, 2016a,b), and quaternion-valued wireless communications (Zetterberg and Brandstrom, 1977;Isaeva and Sarytchev, 1995;Liu, 2014). The most widely used hypercomplex numbers are quaternions, with rigorous physical interpretation for 3-D and 4-D rotational problems (Kantor et al, 1989;Ward, 1997).…”