Application of Super-Resolution Algorithms for the Navigation of Autonomous Mobile Robots
In the paper the idea of using the super-resolution algorithms for the self-localization and vision based navigation of autonomous mobile robots is discussed. Since such task is often limited both by the limited resolution of the mounted video camera as well as the available computational resources, a typical approach for video based navigation of mobile robots, similarly as many small flying robots (drones), is using low resolution cameras equipped with average class lenses. The images captured by such video system should be further processed in order to extract the data useful for real-time control of robot’s motion. In some simplified systems such navigation, especially in the within an enclosed environment (interior), is based on the edge and corner detection and binary image analysis, which could be troublesome for low resolution images.
Considering the possibilities of obtaining higher resolution images from low resolution image sequences, the accuracy of such edge and corner detections may be improved by the application of super-resolution algorithms. In order to verify the usefulness of such approach some experiments have been conducted based on the processing of the captured sequences of the HD images further downsampled and reconstructed using the super-resolution algorithms. Obtained results have been reported in the last section of the paper.
KeywordsMobile Robot Scale Invariant Feature Transform Autonomous Mobile Robot Mobile Robot Navigation Bicubic Interpolation
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