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Minimal solutions for the rotational alignment of IMU-camera systems using homography constraints

Guan, Banglei and Yu, Qifeng and Fraundorfer, Friedrich (2018) Minimal solutions for the rotational alignment of IMU-camera systems using homography constraints. Computer Vision and Image Understanding, 170, pp. 79-91. Elsevier. DOI: 10.1016/j.cviu.2018.03.001 ISSN 1077-3142

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Official URL: https://doi.org/10.1016/j.cviu.2018.03.001

Abstract

In this paper, we explore the different minimal case solutions to the rotational alignment of IMU-camera systems using homography constraints. The assumption that a ground plane is visible in the images can easily be created in many situations. This calibration process is relevant to many smart devices equipped with a camera and an inertial measurement unit (IMU), like micro aerial vehicles (MAVs), smartphones and tablets, and it is a fundamental step for vision and IMU data fusion. Our solutions are novel as they compute the rotational alignment of IMU-camera systems by utilizing a first-order rotation approximation and by solving a polynomial equation system derived from homography constraints. These solutions depend on the calibration case with respect to camera motion (general motion case or pure rotation case) and camera parameters (calibrated camera or partially uncalibrated camera). We then demonstrate that the number of matched points in an image pair can vary from 1.5 to 3. This enables us to calibrate using only one relative movement and provide the exact algebraic solution to the problem. The novel minimal case solutions are useful to reduce the computation time and increase the calibration robustness when using Random Sample Consensus (RANSAC) on the point correspondences between two images. Furthermore, a non-linear parameter optimization over all image pairs is performed. In contrast to the previous calibration methods, our solutions do not require any special hardware, and no problems are experienced with one image pair without special motion. Finally, by evaluating our algorithm on both synthetic and real scene data including data obtained from robots, smartphones and MAVs, we demonstrate that our methods are both efficient and numerically stable for the rotational alignment of IMUcamera systems.

Item URL in elib:https://elib.dlr.de/120649/
Document Type:Article
Title:Minimal solutions for the rotational alignment of IMU-camera systems using homography constraints
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Guan, BangleiCollege of Aerospace Science and Engineering, Nat. University of Defense Technology, ChinaUNSPECIFIED
Yu, QifengHunan Provincial Key Laboratory of Image Measurement and Vision Navigation, ChinaUNSPECIFIED
Fraundorfer, Friedrichfriedrich.fraundorfer (at) dlr.deUNSPECIFIED
Date:February 2018
Journal or Publication Title:Computer Vision and Image Understanding
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:170
DOI :10.1016/j.cviu.2018.03.001
Page Range:pp. 79-91
Publisher:Elsevier
ISSN:1077-3142
Status:Published
Keywords:IMU-camera calibration; Rotational alignment; Minimal solution; Homography constraint; Algebraic solution; Pure rotation
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Transport
HGF - Program Themes:Traffic Management (old)
DLR - Research area:Transport
DLR - Program:V VM - Verkehrsmanagement
DLR - Research theme (Project):V - Vabene++ (old)
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > Photogrammetry and Image Analysis
Deposited By: Zielske, Mandy
Deposited On:03 Jul 2018 18:35
Last Modified:06 Sep 2019 15:19

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