Paper Info
Title
Optimal High-Dynamic-Range Image Acquisition For Humanoid Robots
Abstract
Humanoid robots should be able to visually recognize objects and estimate their 6D pose in real environmental conditions with their limited sensor capabilities. In order to achieve these visual skills, it is necessary to establish an optimal visual transducer connecting the scene layout with the internal representations of objects and places. This visual transducer should capture the noiseless visual manifold of the scene with high-dynamic-range in an efficient manner. Our endeavor is to develop such a visual transducer using the widespread LDR cameras in humanoid robots. In our previous work, the noiseless acquisition of continuous images [1] and the improved radiometric calibration [2] already enabled the humanoid robots to attain the desired visual manifold in terms of quality. However, since the radiance range of the scene can be very wide, the required amount of exposures to capture the visual manifold (robustly without radiance inconsistencies) turns impractically large in terms of scope, granularity and acquisition time. In this article, a method for estimating the minimal amount of exposures and their particular integration times is presented. This method integrates our previous work in order to synthesize HDR images with the minimal amount of exposures while ensuring the high quality of the resulting image. Conclusively, the minimal exposure set provides performance improvements without quality trade-off. Experimental evaluation is presented with the humanoid robots ARMAR-IIIa, b [3].
Year
Venue
Keywords
2013
2013 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
humanoid robots,pose estimation,object recognition
Field
DocType
ISSN
Transducer,Computer vision,Computer science,Pose,Artificial intelligence,Granularity,High dynamic range,Calibration,Radiance,Cognitive neuroscience of visual object recognition,Humanoid robot
Conference
2153-0858
Citations 
PageRank 
References 
0
0.34
8
Authors
3
Name
Order
Citations
PageRank
David Israel Gonzalez-Aguirre1425.94
tamim asfour21889151.86
Rüdiger Dillmann32201262.95