12 accepted papers
Multispectral sensors, which measure multiple wavelength bands beyond the standard red, green, and blue channels, capture richer information than conventional RGB cameras. Such enriched data is especially valuable in visual servoing, where robot control critically depends on image content. However, …
Ensuring connectivity and coordination in heterogeneous multi-robot systems (MRS) navigating complex environments is a critical challenge, especially when communication constraints and obstacles cause robots to become lost or disconnected. This paper presents a novel approach integrating Model Predi
In this paper, we propose a theoretical framework for designing a multi-robot formation equipped with Ultra-wideband (UWB) sensors to localize a target robot. In the presence of noisy range measurements, the accuracy of the target robot’s pose estimation is highly dependent on the chosen formation g
Vehicle-to-everything (V2X) collaborative perception has emerged as a promising solution to address the limitations of single-vehicle perception systems. However, existing V2X datasets are limited in scope, diversity, and quality. To address these gaps, we present Mixed Signals, a comprehensive V2X…
Determining the pose using 3D data captured from two distinct frames holds significant significance in various robotic applications such as odometry, simultaneous localization and mapping and place recognition. Typically, this pose is calculated through the resolution of a least-squares problem, whi
Hybrid visual odometry methods achieve state-of-the-art performance by fusing both data-based deep learning networks and model-based localization approaches. However, these methods also suffer from deep learning domain gap problems, which leads to an accuracy drop of the hybrid visual odometry appro
This paper presents a novel approach for Visual Servoing (VS) using a multispectral camera, where the number of data are more than three times that of a standard color camera. To meet real-time feasibility, the multispectral data captured by the camera are processed using dimensionality reduction te
Stereo-matching is one of the most important low-level visual perception tasks. Currently, two-stage 2D-3D networks are the main solutions. These methods involve creating a cost volume using low-resolution stereo feature maps, which separate the network into a feature net and a matching net. However…
This work focuses on localizing a single target robot with multi-robot formations in 2D space. The cooperative robots employ inter-robot range measurements to assess the target position. In the presence of noisy measurements, the choice of formation geometries significantly impacts the accuracy of t…
Computing the pose from 3D data acquired in two different frames is of high importance for several robotic tasks like odometry, SLAM and place recognition. The pose is generally obtained by solving a least-squares problem given points-to-points, points-to-planes or points to lines correspondences. T
Autonomous mobile robot navigation in a human populated and encumbered environment is recognized as a hard problem to be solved in real-time. Most of the time, robots face the so-called ‘Freezing Robot Problem’, that occurs when the robot stops because no feasible and safe motion can be found. In or…
Visual odometry is an important part of the perception module of autonomous robots. Recent advances in deep learning approaches have given rise to hybrid visual odometry approaches that combine both deep networks and traditional pose estimation methods. One limitation of deep learning approaches is…