The EUMarineRobots (EUMR) project is glad to announce the ninth “Coffee with EUMR” webinar on Thursday, 10 June 2021 at 11:00 CET. This webinar will be hosted by the University of Girona and will consist of two short talks presented by the University of Girona. The first talk titled “Allowing untrained scientists to safely pilot ROV’s: Early collision detection and avoidance using omnidirectional vision” will be given by Eduardo Ochoa and the second talk titled “Extrinsic visual-inertial calibration for motion distortion correction of underwater 3D scans” will be given by Miguel Castillón.
Please use the link below to join the meeting.
The abstracts of the talks and short speakers' biographies follow:
The study of underwater environments involves multiple hazards that can compromise the safety of robots. Underwater missions require a high level of attention from Remotely Operated Vehicle (ROV) operators to avoid damage to the robot. For this reason, there is a growing trend in research to develop systems with new capabilities, such as advanced assisted mapping, spatial awareness and safety, and user immersion. The aim of this work is to devise a system that provides the vehicle with proximity awareness capabilities for navigation in complex environments. By using the advantages of omnidirectional multi-camera systems a much higher level of spatial awareness can be achieved. This paper presents a visual-based multi-camera system which is able to detect the presence of nearby objects in the environment, to create a local map of points, and to assign collision risk values to this map. The system exploits this information to generate warnings when approaching potentially dangerous obstacles and at the same time creates a collision risk map that provides a proximity awareness representation of the environment.
Eduardo Ochoa graduated from Mechatronics Engineering from the don Bosco University in El Salvador and received the MSc. in Computer Vision & Robotics (VIBOT) from the University of Girona in 2019. Currently doing a PhD on the computer vision field at the University of Girona (UdG). His current research is focused on computer vision techniques applied to achieve the goal of avoiding obstacles in underwater environments using ROV/AUV.
Underwater 3D laser scanners are an essential type of sensor used by unmanned underwater vehicles (UUVs) for operations such as navigation, inspection, and object recognition and manipulation. Scanners that acquire 3D data by sweeping a laser plane across the scene can provide very high lateral resolution. However, their data may suffer from rolling shutter effect if the change of pose of the robot with respect to the scanned target during the sweep is not negligible. In order to compensate for motion-related distortions without the need for point cloud postprocessing, the 6-DoF pose at which the scanner acquires each line needs to be accurately known. In the underwater domain, autonomous vehicles are often equipped with a high-end inertial navigation system (INS) that provides reliable navigation data. Nonetheless, the relative pose of the 3D scanner with respect to the inertial reference frame of the robot is not usually known a priori. Therefore, our work uses an ego-motion-based calibration algorithm to calibrate the extrinsic parameters of the visual-inertial sensor pair. The method was evaluated experimentally in laboratory conditions.
Miguel Castillón received the BSc degree in industrial engineering from University of Zaragoza, Spain, in 2015 and the MSc in mechanical engineering from KU Leuven, Belgium, in 2018. He is currently pursuing the PhD degree in robotics at University of Girona, Spain. His research interests include computer vision applied to robotics and autonomous navigation.
For more information about Coffee with EUMR visit https://www.eumarinerobots.eu/news/ninth-coffee-eumr
This project was funded with support from the European Commission. This publication reflects the views only of the author, and neither the European Commission nor the NA DAAD is responsible for any use which may be made of the information contained therein. Project ID: 2018-1-DE01-KA203-004259.
