Experiments in Moving Baseline Navigation using Autonomous Surface Craft

This paper describes an on-going research effort to achieve real-time cooperative localization of multiple autonomous underwater vehicles. We describe a series of experiments that utilize autonomous surface craft (ASC), equiped with undersea acoustic modems, GPS, and 802.11b wireless ethernet communications, to acquire data and develop software for cooperative localization of distributed vehicle networks. Our experiments demonstrate the capability of the Woods Hole acoustic modems to provide accurate round-trip and one-way range measurements, as well as data transfer, for a fully mobile network of vehicles in formation flight. Finally, we present preliminary results from initial experiments involving cooperative operation of an Odyssey III AUV and two ASCs, demonstrating ranging and data transfer from the ASCs to the Odyssey III

Vehicle infrastructure cooperative localization using Factor Graphs

Highly assisted and Autonomous Driving is dependent on the accurate localization of both the vehicle and other targets within the environment. With increasing traffic on roads and wider proliferation of low cost sensors, a vehicle-infrastructure cooperative localization scenario can provide improved performance over traditional mono-platform localization. The paper highlights the various challenges in the process and proposes a solution based on Factor Graphs which utilizes the concept of topology of vehicles. A Factor Graph represents probabilistic graphical model as a bipartite graph. It is used to add the inter-vehicle distance as constraints while localizing the vehicle. The proposed solution is easily scalable for many vehicles without increasing the execution complexity. Finally simulation indicates that incorporating the topology information as a state estimate can improve performance over the traditional Kalman Filter approac

Aerial-Ground collaborative sensing: Third-Person view for teleoperation

Rapid deployment and operation are key requirements in time critical application, such as Search and Rescue (SaR). Efficiently teleoperated ground robots can support first-responders in such situations. However, first-person view teleoperation is sub-optimal in difficult terrains, while a third-person perspective can drastically increase teleoperation performance. Here, we propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide third-person perspective to ground robots. While our approach is based on local visual servoing, it further leverages the global localization of several ground robots to seamlessly transfer between these ground robots in GPS-denied environments. Therewith one MAV can support multiple ground robots on a demand basis. Furthermore, our system enables different visual detection regimes, and enhanced operability, and return-home functionality. We evaluate our system in real-world SaR scenarios.Comment: Accepted for publication in 2018 IEEE International Symposium on Safety, Security and Rescue Robotics (SSRR