TY - GEN

T1 - Set-Membership Extrinsic Calibration of a 3D LiDAR and a Camera

AU - Voges, Raphael

AU - Wagner, Bernardo

N1 - Funding information: The authors are with the Real Time Systems Group (RTS), Institute of Systems Engineering, Leibniz Universität Hannover, D-30167 Hannover, Germany. {voges,wagner}@rts.uni-hannover.de This work was supported by the German Research Foundation (DFG) as part of the Research Training Group i.c.sens [RTG 2159].

PY - 2020

Y1 - 2020

N2 - To fuse information from a 3D Light Detection and Ranging (LiDAR) sensor and a camera, the extrinsic transformation between the sensor coordinate systems needs to be known. Therefore, an extrinsic calibration must be performed, which is usually based on features extracted from sensor data. Naturally, sensor errors can affect the feature extraction process, and thus distort the calibration result. Unlike previous works, which do not consider the uncertainties of the sensors, we propose a set-membership approach that takes all sensor errors into account. Since the actual error distribution of off-the-shelf sensors is often unknown, we assume to only know bounds (or intervals) enclosing the sensor errors and accordingly introduce novel error models for both sensors. Next, we introduce interval-based approaches to extract corresponding features from images and point clouds. Due to the unknown but bounded sensor errors, we cannot determine the features exactly, but compute intervals guaranteed to enclose them. Subsequently, these feature intervals enable us to formulate a Constraint Satisfaction Problem (CSP). Finally, the CSP is solved to find a set of solutions that is guaranteed to contain the true solution and simultaneously reflects the accuracy of the calibration. Experiments using simulated and real data validate our approach and show its advantages over existing methods.

AB - To fuse information from a 3D Light Detection and Ranging (LiDAR) sensor and a camera, the extrinsic transformation between the sensor coordinate systems needs to be known. Therefore, an extrinsic calibration must be performed, which is usually based on features extracted from sensor data. Naturally, sensor errors can affect the feature extraction process, and thus distort the calibration result. Unlike previous works, which do not consider the uncertainties of the sensors, we propose a set-membership approach that takes all sensor errors into account. Since the actual error distribution of off-the-shelf sensors is often unknown, we assume to only know bounds (or intervals) enclosing the sensor errors and accordingly introduce novel error models for both sensors. Next, we introduce interval-based approaches to extract corresponding features from images and point clouds. Due to the unknown but bounded sensor errors, we cannot determine the features exactly, but compute intervals guaranteed to enclose them. Subsequently, these feature intervals enable us to formulate a Constraint Satisfaction Problem (CSP). Finally, the CSP is solved to find a set of solutions that is guaranteed to contain the true solution and simultaneously reflects the accuracy of the calibration. Experiments using simulated and real data validate our approach and show its advantages over existing methods.

UR - http://www.scopus.com/inward/record.url?scp=85101771605&partnerID=8YFLogxK

U2 - 10.1109/IROS45743.2020.9341266

DO - 10.1109/IROS45743.2020.9341266

M3 - Conference contribution

SN - 978-1-7281-6213-3

SP - 9012

EP - 9019

BT - 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2020

CY - Las Vegas, NV, USA

ER -