Localization and Tracking of Uncontrollable Underwater Agents: Particle Filter Based Fusion of On-Body IMUs and Stationary Cameras

Abstract

Tracking of uncontrollable agents in a controlled environment is an important research question for the coordination of controllable and uncontrollable agents and bio-inspired multi-agent control. This paper presents a framework that approaches the multiagent tracking problem from a localization perspective, utilizing a combination of wearable sensors and stationary cameras. Specifically, this framework was applied to localize uncontrollable biological agents (dolphins) in a well defined environment. The biological agents were outfitted with wearable sensors (IMU, speed, depth) and were free to move in their three dimensional habitat. The dynamic data collected by the wearable sensors was supplemented with image data collected using a pair of cameras mounted above the habitat. The framework presented in this paper combines data from these sensor streams to calculate an accurate estimate of the animal's location during extended periods of free movement. The associations between camera detections and tagged agents are handled using a particle filter embedded with a fuzzy observation concept. The platform is readily implementable in similar water / land environments, and is able to handle nonlinear agent dynamics, non-Gaussian noise, and sparse camera observations while maintaining robust agent localization and tracking.