Calibrating Depth Sensors with a Genetic Algorithm

DSpace Repository


Dateien:
Aufrufstatistik

URI: http://hdl.handle.net/10900/87699
http://nbn-resolving.de/urn:nbn:de:bsz:21-dspace-876993
http://dx.doi.org/10.15496/publikation-29085
Dokumentart: Report (Bericht)
Date: 2019-04-11
Language: English
Faculty: 7 Mathematisch-Naturwissenschaftliche Fakultät
Department: Informatik
DDC Classifikation: 004 - Data processing and computer science
Keywords: Algorithmus , Optimierung , Kamera , Stereokamera , Lidar
Other Keywords:
stereo vision
depth sensor calibration
genetic algorithm
kitti
optimization
License: Publishing license including print on demand
Order a printed copy: Print-on-Demand
Show full item record

Inhaltszusammenfassung:

In this report, we deal with the optimization of the transformation estimate between the coordinate systems of depth sensors, \ie sensors that produce 3D measurements. For that, we present a novel method using a genetic algorithm to refine the six degrees of freedom (6 DoF) transformation via three rotational and three translational offsets. First, we demonstrate the necessity for an accurate depth sensor calibration using a depth error model of stereo cameras. The fusion of stereo disparity assumes a Gaussian disparity error distribution, which we examine with different stereo matching algorithms on the widely-used KITTI visual odometry dataset. Our analysis shows that the existing calibration is not adequate for accurate disparity fusion. As a consequence, we employ our genetic algorithm on this particular dataset, which results in a greatly improved calibration between the mounted stereo camera and the Lidar. Thus, stereo disparity estimates show improved results in quantitative evaluations.

This item appears in the following Collection(s)