Appearance-based odometry and mapping with feature descriptors for underwater robots

Abstract

Abstract:The use of Autonomous Underwater Vehicles (AUVs) for underwater tasks is a promising robotic field. These robots can carry visual inspection cameras. Besides serving the activities of inspection and mapping, the captured images can also be used to aid navigation and localization of the robots. Visual odometry is the process of determining the position and orientation of a robot by analyzing the associated camera images. It has been used in a wide variety of non-standard locomotion robotic methods. In this context, this paper proposes an approach to visual odometry and mapping of underwater vehicles. Supposing the use of inspection cameras, this proposal is composed of two stages: i) the use of computer vision for visual odometry, extracting landmarks in underwater image sequences and ii) the development of topological maps for localization and navigation. The integration of such systems will allow visual odometry, localization and mapping of the environment. A set of tests with real robots was accomplished, regarding online and performance issues. The results reveals an accuracy and robust approach to several underwater conditions, as illumination and noise, leading to a promissory and original visual odometry and mapping technique.

Object Classification in Semi Structured Enviroment Using Forward-Looking Sonar

“…However, very few work has been achieved in underwater robotics where the semantic knowledge of the environment could be applied, for instance, to predict changes and to make high-level decisions. In fact, the mapping problem in underwater robots has been addressed typically by only using geometric information with sonar or Red-Green-Blue (RGB) sensors [3,4,5]. …”