Related papers: Decentralized Cooperative Localization for Multi-R…
Place recognition is an essential and challenging task in loop closing and global localization for robotics and autonomous driving applications. Benefiting from the recent advances in deep learning techniques, the performance of LiDAR place…
Collaborative Localisation has been studied extensively in recent years as a way to improve pose estimation of unmanned aerial vehicles in challenging environments. However little attention has been paid toward advancing the underlying…
In this study, we propose a novel visual localization approach to accurately estimate six degrees of freedom (6-DoF) poses of the robot within the 3D LiDAR map based on visual data from an RGB camera. The 3D map is obtained utilizing an…
Cooperative localization is fundamental to autonomous multirobot systems, but most algorithms couple inter-robot communication with observation, making these algorithms susceptible to failures in both communication and observation steps. To…
In this paper we propose a modular nonlinear least squares filtering approach for systems composed of independent subsystems. The state and error covariance estimate of each subsystem is updated independently, even when a relative…
One of the crucial problems in robotic swarm-based operation is to search and neutralize heterogeneous targets in an unknown and uncertain environment, without any communication within the swarm. Here, some targets can be neutralized by a…
In environments where multiple robots must coordinate in a shared space, decentralized approaches allow for decoupled planning at the cost of global guarantees, while centralized approaches make the opposite trade-off. These solutions make…
Recent advancements in remote sensing (RS) technologies have shown their potential in accurately classifying local climate zones (LCZs). However, traditional scene-level methods using convolutional neural networks (CNNs) often struggle to…
Long-horizon collaborative vision-language navigation (VLN) is critical for multi-robot systems to accomplish complex tasks beyond the capability of a single agent. CoNavBench takes a first step by introducing the first collaborative…
The mobile robot relies on SLAM (Simultaneous Localization and Mapping) to provide autonomous navigation and task execution in complex and unknown environments. However, it is hard to develop a dedicated algorithm for mobile robots due to…
The field of collaborative robotics and human-robot interaction often focuses on the prediction of human behaviour, while assuming the information about the robot setup and configuration being known. This is often the case with fixed…
We study the problem of multi-robot mapless navigation in the popular Centralized Training and Decentralized Execution (CTDE) paradigm. This problem is challenging when each robot considers its path without explicitly sharing observations…
Localization is a fundamental task in robotics for autonomous navigation. Existing localization methods rely on a single input data modality or train several computational models to process different modalities. This leads to stringent…
Reinforcement learning (RL) algorithms can find an optimal policy for a single agent to accomplish a particular task. However, many real-world problems require multiple agents to collaborate in order to achieve a common goal. For example, a…
Multi-robot teams can achieve more dexterous, complex and heavier payload tasks than a single robot, yet effective collaboration is required. Multi-robot collaboration is extremely challenging due to the different kinematic and dynamics…
This paper presents the design and implementation of a perceptual system for the mobile robot using modern sensors and multi-point communication channels. The data extracted from the perceptual system is processed by a sensor fusion model…
The role of deep learning (DL) in robotics has significantly deepened over the last decade. Intelligent robotic systems today are highly connected systems that rely on DL for a variety of perception, control, and other tasks. At the same…
Autonomous robots operating in open environments need the ability to continuously handle tasks that are not covered by predefined local methods. However, existing approaches often rely on repeated large-language-model (LLM) interaction for…
In recent years, the mobile robot has been the concern of numerous researcher since they are widely applied in various fields of daily life. This paper applies a virtual robot operating system (ROS) platform to develop a localization system…
This paper introduces a novel approach for modeling the dynamics of soft robots, utilizing a differentiable filter architecture. The proposed approach enables end-to-end training to learn system dynamics, noise characteristics, and temporal…