针对低成本室内移动机器人中毫米波雷达点云稀疏、噪声和误检测问题，本文提出轻量级图神经网络框架RaGNNarok，在Raspberry Pi 5上仅需7.3毫秒推理时间即可实时增强雷达点云，并在定位、SLAM和自主导航任务中验证了其可靠性和泛化能力。
摘要：
随着家庭和商业空间中自动化应用的日益普及，低成本室内移动机器人越来越受欢迎。然而，现有的基于激光雷达和摄像头的解决方案存在局限性，例如在视觉遮挡环境中性能不佳、数据处理的计算开销高以及激光雷达成本高昂。相比之下，毫米波雷达传感器提供了一种成本效益高且轻量化的替代方案，能够不受能见度影响提供精确测距。然而，现有的基于雷达的定位存在点云生成稀疏、噪声和误检测等问题。因此，在本工作中，我们提出了RaGNNarok，一个实时、轻量且具有泛化能力的基于图神经网络（GNN）的框架，用于增强雷达点云，即使在复杂和动态环境中也能有效工作。RaGNNarok在低成本Raspberry Pi 5上的推理时间仅为7.3毫秒，即使在资源受限的设备上也能高效运行，无需额外的计算资源。我们在三个不同环境中评估了其在定位、SLAM和自主导航等关键任务上的性能。结果表明RaGNNarok具有强大的可靠性和泛化能力，使其成为低成本室内移动机器人的稳健解决方案。
英文摘要：
Low-cost indoor mobile robots have gained popularity with the increasing adoption of automation in homes and commercial spaces. However, existing lidar and camera-based solutions have limitations such as poor performance in visually obscured environments, high computational overhead for data processing, and high costs for lidars. In contrast, mmWave radar sensors offer a cost-effective and lightweight alternative, providing accurate ranging regardless of visibility. However, existing radar-based localization suffers from sparse point cloud generation, noise, and false detections. Thus, in this work, we introduce RaGNNarok, a real-time, lightweight, and generalizable graph neural network (GNN)-based framework to enhance radar point clouds, even in complex and dynamic environments. With an inference time of just 7.3 ms on the low-cost Raspberry Pi 5, RaGNNarok runs efficiently even on such resource-constrained devices, requiring no additional computational resources. We evaluate its performance across key tasks, including localization, SLAM, and autonomous navigation, in three different environments. Our results demonstrate strong reliability and generalizability, making RaGNNarok a robust solution for low-cost indoor mobile robots.
Deep reinforcement learning (RL), where the agent learns from mistakes, has been successfully applied to a variety of tasks. With the aim of learning collision-free policies for unmanned vehicles, deep RL has been used for training with various types of data, such as colored images, depth images, and LiDAR point clouds, without the use of classic map--localize--plan approaches. However, existing methods are limited by their reliance on cameras and LiDAR devices, which have degraded sensing under adverse environmental conditions (e.g., smoky environments). In response, we propose the use of sin…
WaterVG: Waterway Visual Grounding based on Text-Guided Vision and mmWave Radar
Institute of Deep Perception Tech, JITRI（JITRI深度感知技术研究所）；School of Advanced Technology, Xi’an Jiaotong-Liverpool University（西交利物浦大学先进技术学院）；Faculty of Science and Engineering, University of Liverpool（利物浦大学科学与工程学院）；Thrust of Artificial Intelligence and Thrust of Intelligent Transportation, HKUST (Guangzhou)（香港科技大学（广州）人工智能学科及智能交通学科）
Unmanned surface vehicles (USVs) have great value with their ability to execute hazardous and time-consuming missions over water surfaces. Recently, USVs for inland waterways have attracted increasing attention for their potential application in autonomous monitoring, transportation, and cleaning. However, unlike sailing in open water, the challenges posed by scenes of inland waterways, such as the complex distribution of obstacles, the global positioning system (GPS) signal denial environment, the reflection of bank-side structures, and the fog over the water surface, all impede USV applicati…
Achelous++: Power-Oriented Water-Surface Panoptic Perception Framework on Edge Devices based on Vision-Radar Fusion and Pruning of Heterogeneous Modalities
University of Liverpool（利物浦大学）；Xi’an Jiaotong-Liverpool University（西交利物浦大学）；JITRI（江苏省产业技术研究院）；Nantong University（南通大学）
DEI - CNIT University of Bologna（博洛尼亚大学 DEI - CNIT）；CNR-IEIIT（意大利国家研究委员会电子、信息工程与电信研究所）；ECE Department Stony Brook University（石溪大学电子与计算机工程系）
In this paper, we study a joint detection, mapping and navigation problem for a single unmanned aerial vehicle (UAV) equipped with a low complexity radar and flying in an unknown environment. The goal is to optimize its trajectory with the purpose of maximizing the mapping accuracy and, at the same time, to avoid areas where measurements might not be sufficiently informative from the perspective of a target detection. This problem is formulated as a Markov decision process (MDP) where the UAV is an agent that runs either a state estimator for target detection and for environment mapping, and a…
Micro unmanned aerial vehicles (mUAV) became very common in recent years. As a result of their widespread usage, when they are flown by hobbyists illegally, crucial risks are imposed and such mUAVs need to be sensed by security systems. Furthermore, the sensing of mUAVs are essential for also swarm robotics research where the individuals in a flock of robots require systems to sense and localize each other for coordinated operation. In order to obtain such systems, there are studies to detect mUAVs utilizing different sensing mediums, such as vision, infrared and sound signals, and small-scale…
Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University, College Station, TX, USA（迈克·沃克66机械工程系，德克萨斯A&M大学，学院站，德克萨斯州，美国）；DEVCOM Army Research Laboratory, Adelphi, MD, USA（陆军研究实验室，阿德菲，马里兰州，美国）；University of Texas at Austin Center for Autonomy, Austin, TX, USA（德克萨斯大学奥斯汀分校自主性中心，奥斯汀，德克萨斯州，美国）
ASVSim (AirSim for Surface Vehicles): A High-Fidelity Simulation Framework for Autonomous Surface Vehicle Research
IDLab, Faculty of Applied Engineering, University of Antwerp - imec（ID实验室，应用工程学院，安特卫普大学 - imec）；Cosys-Lab, Faculty of Applied Engineering, University of Antwerp（Cosys实验室，应用工程学院，安特卫普大学）；Flanders Make Strategic Research Centre（弗拉芒制造战略研究中心）
A multi-robot system for the detection of explosive devices
Royal Military Academy of Belgium（比利时皇家军事学院）；Advanced Center for Aerospace Technologies (CATEC)（航空航天技术高级中心）；Space Applications Services NV/SA（空间应用服务公司）；Lightnovo ApS（莱特诺沃公司）；Scientific Council HCR—Center for Testing, Development, and Training（HCR科学委员会测试、开发与培训中心）
Heriot-Watt University（赫瑞-瓦特大学）；The University of Manchester（曼彻斯特大学）；Nagasaki University（长崎大学）
Mobile robot autonomy has made significant advances in recent years, with navigation algorithms well developed and used commercially in certain well-defined environments, such as warehouses. The common link in usage scenarios is that the environments in which the robots are utilized have a high degree of certainty. Operating environments are often designed to be robot friendly, for example augmented reality markers are strategically placed and the ground is typically smooth, level, and clear of debris. For robots to be useful in a wider range of environments, especially environments that are n…
