Stereo vision for robotic applications in the presence of non-ideal lighting conditions

Many robotic and machine-vision applications rely on the accurate results of stereo correspondence algorithms. However, difficult environmental conditions, such as differentiations in illumination depending on the viewpoint, heavily affect the stereo algorithms' performance. This work proposes a new illumination-invariant dissimilarity measure in order to substitute the established intensity-based ones. The proposed measure can be adopted by almost any of the existing stereo algorithms, enhancing it with its robust features. The performance of the dissimilarity measure is validated through experimentation with a new adaptive support weight (ASW) stereo correspondence algorithm. Experimental results for a variety of lighting conditions are gathered and compared to those of intensity-based algorithms. The algorithm using the proposed dissimilarity measure outperforms all the other examined algorithms, exhibiting tolerance to illumination differentiations and robust behavior.

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