Optimal high-dynamic-range image acquisition for humanoid robots

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 radio-metric 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-III a, b [3].