Object Recognition and Pose Estimation using Laser scans For Advanced Underwater Manipulation

Abstract

3D object recognition is an active research area in computer vision and robotics. The integration of spatial information with semantic knowledge has become an important task for robots in order to successfully perform autonomous intervention missions. This paper presents an approach for the recognition and pose estimation of underwater objects, with the goal of enabling autonomous underwater intervention in man-made structures. The methods are developed to be used with raw data consisting of 3D colorless point clouds collected by a fast laser scanner. The proposed approach contains two main phases: Object recognition from range data, and feature-based semantic SLAM. The first goal consists of recognizing different objects present in the scene. For this purpose, a recognition and pose estimation pipeline was developed enclosing different steps such as segmentation, identification, and estimation of the position and orientation for each targeted object. The second goal aims at improving the AUV navigation in an underwater environment by using the result of the recognition and pose estimation pipeline to feed a feature based SLAM algorithm. As the AUV moves along the trajectory, the SLAM algorithm builds a map, recognizes targeted objects and integrates them into this map, and localizes its position with respect to it. Compared to previous experimental results performed in a water tank, this paper emphasizes the importance of estimating the pose of the objects (namely the orientation), as a way of promoting the accuracy of the robot localization.