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Multi‐Modal Sensor Calibration Using a Gradient Orientation Measure
Author(s) -
Taylor Zachary,
Nieto Juan,
Johnson David
Publication year - 2015
Publication title -
journal of field robotics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.152
H-Index - 96
eISSN - 1556-4967
pISSN - 1556-4959
DOI - 10.1002/rob.21523
Subject(s) - metric (unit) , calibration , computer vision , artificial intelligence , orientation (vector space) , computer science , lidar , measure (data warehouse) , modal , remote sensing , range (aeronautics) , mathematics , engineering , geography , data mining , statistics , operations management , geometry , aerospace engineering , polymer chemistry , chemistry
This paper presents a new metric for automated registration of multi‐modal sensor data. The metric is based on the alignment of the orientation of gradients formed from the two candidate sensors. Data registration is performed by estimating the sensors' extrinsic parameters that minimises the misalignment of the gradients. The metric can operate in a large range of applications working on both 2D and 3D sensor outputs and is suitable for both (i) single scan data registration and (ii) multi‐sensor platform calibration using multiple scans. Unlike traditional calibration methods, it does not require markers or other registration aids to be placed in the scene. The effectiveness of the new method is demonstrated with experimental results on a variety of camera‐lidar and camera‐camera calibration problems. The novel metric is validated through comparisons with state of the art methods. Our approach is shown to give high quality registrations under all tested conditions.