16 accepted papers
This document presents the outcomes of the Dagstuhl Seminar "Roadmap for Responsible Robotics," held in September 2023 at the Leibniz Centre for Informatics, Schloss Dagstuhl, Germany. The seminar brought together researchers from Robotics, Computer Science, Social and Cognitive Sciences, and Philos…
Compared to conventional cameras, event cameras provide a high dynamic range and low latency, offering greater robustness to rapid motion and challenging lighting conditions. Although the potential of event cameras for visual place recognition (VPR) has been established, the development of robust VP…
Event-based localization research and datasets are a rapidly growing area of interest, with a tenfold increase in the cumulative total number of published papers on this topic over the past 10 years. Whilst the rapid expansion in the field is exciting, it brings with it an associated challenge: a gr…
Visual Place Recognition (VPR) in mobile robotics enables robots to localize themselves by recognizing previously visited locations using visual data. While the reliability of VPR methods has been extensively studied under conditions such as changes in illumination, season, weather and viewpoint, th…
Visual Place Recognition (VPR) enables systems to identify previously visited locations within a map, a fundamental task for autonomous navigation. Prior works have developed VPR solutions using event cameras, which asynchronously measure per-pixel brightness changes with microsecond temporal resolu…
Visual Place Recognition (VPR) is fundamental for the global re-localization of robots and devices, enabling them to recognize previously visited locations based on visual inputs. This capability is crucial for maintaining accurate mapping and localization over large areas. Given that VPR methods ne…
Visual Place Recognition (VPR) localizes a query image by matching it against a database of geo-tagged reference images, making it essential for navigation and mapping in robotics. Although Vision Transformer (ViT) solutions deliver high accuracy, their large models often exceed the memory and compu…
Visual Place Recognition (VPR) localizes a query image by matching it against a database of geo-tagged reference images, making it essential for navigation and mapping in robotics. Although Vision Transformer (ViT) solutions deliver high accuracy, their large models often exceed the memory and compu
For safety critical applications the ability of localization systems to self-assess their own performance and know when they are failing is as important as absolute accuracy. Previous methods have self-assessed current system performance, identifying failure after it occurs. We propose to instead pr
"Visual Place Recognition (VPR) is a crucial component of many visual localization pipelines for embodied agents. VPR is often formulated as an image retrieval task aimed at jointly learning local features and an aggregation method. The current state-of-the-art VPR methods rely on VLAD aggregation,…
Surgical assistive robots offer the potential for drastically improved patient outcomes through more accurate, more repeatable surgical procedures like shoulder arthroplasty operations. Existing robotic systems typically rely on optical marker tracking and require invasive marker attachment for loca…
Recent advances in image description and matching allowed significant improvements in Visual Place Recognition (VPR). The wide variety of methods proposed so far and the increase of the interest in the field have rendered the problem of evaluating VPR methods an important task. As part of the locali
Localization is a critical capability for robots, drones, and autonomous vehicles operating in a wide range of environments. One of the critical considerations for designing, training, or calibrating visual localization systems is the coverage of the visual sensors equipped on the platforms. In an a
Visual localization algorithms have achieved significant improvements in performance thanks to recent advances in camera technology and vision-based techniques. However, there remains one critical caveat: all current approaches that are based on image retrieval currently scale at best linearly with
Robustness to variations in environmental conditions and camera viewpoint is essential for long-term place recognition, navigation and SLAM. Existing systems typically solve either of these problems, but invariance to both remains a challenge. This paper presents a training-free approach to lateral…
This paper is concerned with the interpretation of visual information for robot localization. It presents a probabilistic localization system that generates an appropriate observation model online, unlike existing systems which require pre-determined belief models. This paper proposes that probabili…