Authors:
Safa Ouerghi
1
;
Remi Boutteau
2
;
Xavier Savatier
2
and
Fethi Tlili
1
Affiliations:
1
Sup’Com and GRESCOM, Tunisia
;
2
ESIGELEC and IRSEEM, France
Keyword(s):
Egomotion, Structure from Motion, Robotics, CUDA, GPU.
Related
Ontology
Subjects/Areas/Topics:
Active and Robot Vision
;
Computer Vision, Visualization and Computer Graphics
;
Image Formation and Preprocessing
;
Image Generation Pipeline: Algorithms and Techniques
;
Motion, Tracking and Stereo Vision
;
Stereo Vision and Structure from Motion
Abstract:
Egomotion estimation is a fundamental issue in structure from motion and autonomous navigation for mobile
robots. Several camera motion estimation methods from a set of variable number of image correspondances
have been proposed. Five-point methods represent the minimal number of required correspondences to estimate
the essential matrix, raised special interest for their application in a hypothesize-and-test framework.
This algorithm allows relative pose recovery at the expense of a much higher computational time when dealing
with higher ratios of outliers. To solve this problem with a certain amount of speedup, we propose in this
work, a CUDA-based solution for the essential matrix estimation performed using the Grobner basis version
of 5-point algorithm, complemented with robust estimation. The description of the hardware-specific implementation
considerations as well as the parallelization methods employed are given in detail. Performance
analysis against existing CPU implementati
on is also given, showing a speedup 4 times faster than the CPU
for an outlier ratio e = 0.5, common for the essential matrix estimation from automatically computed point
correspondences. More speedup was shown when dealing with higher outlier ratios.
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