Abstract
The use of underwater autonomous vehicles has been growing, allowing the performance of tasks that cause inherent risks to Human, namely in inspection processes near to structures. With growth in usage of systems with autonomous navigation, visual acquisition methods have also gotten more developed because, they have appealing cost and they also show interesting results when operate at a short distance. It is possible to improve the quality of navigation through visual SLAM techniques which can map and locate simultaneously and its key aspect is the detection of revisited areas. These techniques are not usually applied to underwater scenarios and, therefore, its performance in environment is unknown. The paper presents a more reliable navigation system for underwater vehicles, resorting to some visual SLAM techniques from literature. The results, conducted in a realistic scenario, demonstrated the ability of the system to be applied to underwater environment.
Highlights
The growing use of underwater autonomous vehicles (AUV) is related with the fact of its actual features (Wynn et al 2014) allow its application in tasks that may involve risks for Human, such as environment monitoring, inspection and demining
This paper presents a robust, accurate and efficient visual system for simultaneous navigation and mapping in underwater environment
For loop-closure detection, the RTAB-MAP and ORB-SLAM2 use vocabulary approaches, that evaluate the similarity between the current frame and the others
Summary
The growing use of underwater autonomous vehicles (AUV) is related with the fact of its actual features (Wynn et al 2014) allow its application in tasks that may involve risks for Human, such as environment monitoring, inspection and demining. The SLAM approach with visual sensors (Pi et al 2014) has the main goal to estimate the camera motion while reconstructs the environment. We present a comparative analysis between vocabulary and Bundle Adjustment approaches to detect revisited areas. To recognize revisited areas and, so, to allow a more reliable motion estimation, a vocabulary method was developed.
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