Sonar-Based Detection and Tracking of a Diver for Underwater Human-Robot Interaction Scenarios

Abstract

An underwater robotic assistant could help a human diver by illuminating work areas, fetching tools from the surface, or monitoring the diver's activity for abnormal behavior. However, in order for basic Underwater Human-Robot Interaction (UHRI) to be successful, the robotic assistant has to first be able to detect and track the diver. This paper discusses the detection and tracking of a diver with a high-frequency forward-looking sonar. The first step in the diver detection involves utilizing classical 2D image processing techniques to segment moving objects in the sonar image. The moving objects are then passed through a blob detection algorithm, and then the blob clusters are processed by the cluster classification process. Cluster classification is accomplished by matching observed cluster trajectories with trained Hidden Markov Models (HMM), which results in a cluster being classified as either a diver or clutter. Real-world results show that a moving diver can be autonomously distinguished from stationary objects in a noisy sonar image and tracked.