Robot calibration and metrology systems vary widely in performance, but, as a general rule, they are considered to be expensive systems that are normally beyond the budget of the average company. A market survey involving some of the leading systems available reveals that the leading performers are characteristically easy to set‐up, operate and, most important, more economical. Nevertheless, the price range of these systems is still too high for them to be in widespread, regular use. The development of systems that combine these characteristics, but at a low‐cost, would fill an important void in the automated manufacturing industry.
Modern visual SLAM (vSLAM) algorithms take advantage of computer vision developments in image processing and in interest point detectors to create maps and trajectories from camera images. Different feature detectors and extractors have been evaluated for this purpose in air and ground environments, but not extensively for underwater scenarios. In this paper (I) we characterize underwater images where light and suspended particles alter considerably the images captured, (II) evaluate the performance of common interest points detectors and descriptors in a variety of underwater scenes and conditions towards vSLAM in terms of the number of features matched in subsequent video frames, the precision of the descriptors and the processing time. This research justifies the usage of feature detectors in vSLAM for underwater scenarios and present its challenges and limitations.
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