Pose Selection for Underwater Object Detection, Pose Estimation, and Tracking

Abstract

Remotely Operated Vehicles (ROVs) are essential instruments in most industrial applications of subsea Inspection, Maintenance, and Repair (IMR). In IMR applications, especially in short-distance inspection and intervention operations, ROVs should be able to find its position and orientation with respect to underwater platforms or specific objects on the platform. Current work class ROVs are normally operated by two pilots. Improving the situational awareness and sensing capabilities is an essential necessity from industrial actors in such a challenging environment. Automated visual based interpretation shows promising results and essential tools toward higher level of autonomy within IMR and ROV operations. Motivated by the large amount of available information in ROV cameras, this article presents a pipeline for object detection, 6D pose estimation and tracking. The main contribution of the paper is its new detection procedure where the current tracker state is evaluated for acceptance or rejection of the new detection. The decomposed tasks of detection, 6D pose estimation and tracking in the proposed pipeline is tested on an open underwater dataset featuring pose annotated objects used for ROV intervention. The results show that the method is highly suitable for supervised autonomy and a step forward to autonomous operations in underwater IMR applications.

Keywords

• Detection
• monocular
• pose estimation
• remotely operated vehicles
• tracking
• underwater