Improving Robot Transparency: An Investigation With Mobile Augmented Reality

Autonomous robots can be difficult to understand by their developers, let alone by end users. Yet, as they become increasingly integral parts of our societies, the need for afford- able easy to use tools to provide transparency grows. The rise of the smartphone and the improvements in mobile computing performance have gradually allowed Augmented Reality (AR) to become more mobile and affordable. In this paper we review relevant robot systems architecture and propose a new software tool to provide robot transparency through the use of AR technology. Our new tool, ABOD3-AR provides real-time graphical visualisation and debugging of a robot’s goals and priorities as a means for both designers and end users to gain a better mental model of the internal state and decision making processes taking place within a robot. We also report on our on-going research programme and planned studies to further understand the effects of transparency to naive users and experts

Towards Outdoor Collaborative Mixed Reality: Lessons Learnt from a Prototype System

Most research on collaborative mixed reality (CMR) has focused on indoor spaces. In this paper, we present our ongoing work aimed at investigating the potential of CMR in outdoor spaces. These spaces present unique challenges due to their larger and more complex nature, particularly in terms of reconstruction, tracking, and interaction. Our prototype system utilises a photorealistic model to facilitate collaboration between remote virtual reality (VR) users and a local augmented reality (AR) user. We discuss our design considerations, lessons learnt, and areas for future work

Dataset for "ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay"

This is the dataset that accompanies our publication "ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay”. The data was collected over a period of time using a custom ARCore based android app. It depicts a retail aisle. The images can be found in the sub-folders “only_jpgs”. The rest of the ARCore data such as camera poses can be found in “data_all” subfolders for each day data was collected for. The data can be used to run the benchmarks from the original paper. It can also be used to reconstruct points clouds using SFM (structure from motion) software.The data was collected over a number of weeks in a local grocery shop. It includes text files listing the 6DOF poses of the phone, and RGB frames. The frames and text files were acquired with a Google Pixel 2 phone, and the RGB frames were captured every 0.5 seconds at a resolution of 640 by 480.A Google Pixel 2 phone was used for the collection of the data. The frames captures are the default camera frames that ARCore provides, under the name "CPU Images". The were stored locally on the phone and then extracted for use in our publication, "ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay."The data is provided in text files and RGB images, in a jpg format. The text files include additional information such as local poses

Robots That Make Sense : Transparent Intelligence Through Augmented Reality

Autonomous robots can be difficult to understand by their develop-ers, let alone by end users. Yet, as they become increasingly integralparts of our societies, the need for affordable easy to use tools toprovide transparency grows. The rise of the smartphone and theimprovements in mobile computing performance have graduallyallowed Augmented Reality (AR) to become more mobile and afford-able. In this paper we review relevant robot systems architectureand propose a new software tool to provide robot transparencythrough the use of AR technology. Our new tool, ABOD3-AR pro-vides real-time graphical visualisation and debugging of a robot’sgoals and priorities as a means for both designers and end usersto gain a better mental model of the internal state and decisionmaking processes taking place within a robot. We also report onour on-going research programme and planned studies to furtherunderstand the effects of transparency to naive users and experts

Robots That Make Sense : Transparent Intelligence Through Augmented Reality

Autonomous robots can be difficult to understand by their develop-ers, let alone by end users. Yet, as they become increasingly integralparts of our societies, the need for affordable easy to use tools toprovide transparency grows. The rise of the smartphone and theimprovements in mobile computing performance have graduallyallowed Augmented Reality (AR) to become more mobile and afford-able. In this paper we review relevant robot systems architectureand propose a new software tool to provide robot transparencythrough the use of AR technology. Our new tool, ABOD3-AR pro-vides real-time graphical visualisation and debugging of a robot’sgoals and priorities as a means for both designers and end usersto gain a better mental model of the internal state and decisionmaking processes taking place within a robot. We also report onour on-going research programme and planned studies to furtherunderstand the effects of transparency to naive users and experts

Dataset for "ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay"

This is the dataset that accompanies our publication "ExMaps: Long-Term Localization in Dynamic Scenes using Exponential Decay”. The data was collected over a period of time using a custom ARCore based android app. It depicts a retail aisle. The images can be found in the sub-folders “only_jpgs”. The rest of the ARCore data such as camera poses can be found in “data_all” subfolders for each day data was collected for. The data can be used to run the benchmarks from the original paper. It can also be used to reconstruct points clouds using SFM (structure from motion) software