Public perceptions of Robotics and Artificial Intelligence (RAI) are important in the acceptance, uptake, government regulation and research funding of this technology. Recent research has shown that the public's understanding of RAI can be negative or inaccurate. We believe effective public engagement can help ensure that public opinion is better informed. In this paper, we describe our first iteration of a high throughput in-person public engagement activity. We describe the use of a light touch quiz-format survey instrument to integrate in-the-wild research participation into the engagement, allowing us to probe both the effectiveness of our engagement strategy, and public perceptions of the future roles of robots and humans working in dangerous settings, such as in the off-shore energy sector. We critique our methods and share interesting results into generational differences within the public's view of the future of Robotics and AI in hazardous environments. These findings include that older peoples' views about the future of robots in hazardous environments were not swayed by exposure to our exhibit, while the views of younger people were affected by our exhibit, leading us to consider carefully in future how to more effectively engage with and inform older people.

Autonomous vehicles and robots are increasingly being deployed to remote, dangerous environments in the energy sector, search and rescue and the military. As a result, there is a need for humans to interact with these robots to monitor their tasks, such as inspecting and repairing offshore wind-turbines. Conversational Agents can improve situation awareness and transparency, while being a hands-free medium to communicate key information quickly and succinctly. As part of our user-centered design of such systems, we conducted an in-depth immersive qualitative study of twelve marine research scientists and engineers, interacting with a prototype Conversational Agent. Our results expose insights into the appropriate content and style for the natural language interaction and, from this study, we derive nine design recommendations to inform future Conversational Agent design for remote autonomous systems.

Visual place recognition is a fundamental problem for many vision based applications. Sparse feature and deep learning based methods have been successful and dominant over the decade. However, most of them do not explicitly leverage high-level semantic information to deal with challenging scenarios where they may fail. This paper proposes a novel visual place recognition algorithm, termed TextPlace, based on scene texts in the wild. Since scene texts are high-level information invariant to illumination changes and very distinct for different places when considering spatial correlation, it is beneficial for visual place recognition tasks under extreme appearance changes and perceptual aliasing. It also takes spatial-temporal dependence between scene texts into account for topological localization. Extensive experiments show that TextPlace achieves state-of-the-art performance, verifying the effectiveness of using high-level scene texts for robust visual place recognition in urban areas.

Global localization lies at the heart of autonomous navigation and Simultaneous Localization and Mapping (SLAM). The appearance-based approach has been successful, but still faces many open challenges in environments where visual conditions vary significantly over time. In this paper, we propose an integrated solution to leverage object-level dense semantics and spatial understanding of the environment for global localization. Our approach models an environment with 3D dense semantics, semantic graph and their topology. This object-level representation is then used for place recognition via semantic object association, followed by 6-DoF pose estimation by the semantic-level point alignment. Extensive experiments show that our approach can achieve robust global localization under extreme appearance changes. It is also capable of coping with other challenging scenarios, such as dynamic environments and incomplete query observations.

This paper describes the design and implementation of a model-based sonar servoing control scheme for Autonomous Underwater Vehicles (AUVs). The proposed controller is designed for autonomous surveillance of underwater structures and it is robust against external disturbances and parametric uncertainties in the AUV dynamic model. The sensor suite includes a Multi-beam Imaging Sonar which provides measurements to a RANSAC-based algorithm for structure detection and pose estimation of the vehicle with respect to the structure. The sonar-based pose estimation is properly fused with the rest of the state measurements provided by a navigation module and the resulted state vector is incorporated as feedback to the controller. The proposed control scheme has analytically guaranteed stability and convergence properties, while its applicability and performance are experimentally verified using the Nessie VI AUV in the presence of external disturbances (medium height waves).