17 accepted papers
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) 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) enables robots and autonomous vehicles to identify previously visited locations by matching current observations against a database of known places. However, VPR systems face significant challenges when deployed across varying visual environments, lighting conditions,
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) 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) enables autonomous systems to localize themselves within an environment using image information. While VPR techniques built upon a Convolutional Neural Network (CNN) backbone dominate state-of-the-art VPR performance, their high computational requirements make them uns
Visual Place Recognition (VPR) is a critical task for performing global re-localization in visual perception systems, requiring the ability to recognize a previously visited location under variations such as illumination, occlusion, appearance and viewpoint. In the case of robotics, the target devic
Recently several fusion and switching based approaches have been presented to solve the problem of Visual Place Recognition. In spite of these systems demonstrating significant boost in VPR performance they each have their own set of limitations. The multi-process fusion systems usually involve empl…
State-of-the-art visual place recognition performance is currently being achieved utilizing deep learning based approaches. Despite the recent efforts in designing lightweight convolutional neural network based models, these can still be too expensive for the most hardware restricted robot applicati
VPR is a fundamental task for autonomous navigation as it enables a robot to localize itself in the workspace when a known location is detected. Although accuracy is an essential requirement for a VPR technique, computational and energy efficiency are not less important for real-world applications.…
Scene classification is a well-established area of computer vision research that aims to classify a scene image into pre-defined categories such as playground, beach and airport. Recent work has focused on increasing the variety of pre-defined categories for classification, but so far failed to cons…
Visual place recognition (VPR) - a fundamental task in computer vision and robotics - is the problem of identifying a place mainly based on visual information. View-point and appearance changes, such as due to weather and seasonal variations, make this task challenging. Currently, there is no univer
Visual place recognition (VPR) is the problem of recognising a previously visited location using visual information. Many attempts to improve the performance of VPR methods have been made in the literature. One approach that has received attention recently is the multi-process fusion where different
This letter presents a novel, compute-efficient and training-free approach based on Histogram-of-OrientedGradients (HOG) descriptor for achieving state-of-the-art performance-per-compute-unit in Visual Place Recognition (VPR). The inspiration for this approach (namely CoHOG) is based on the convolut
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