Real-time and high-precision situational awareness technology is critical for autonomous navigation of unmanned surface vehicles (USVs). In particular, robust and fast obstacle semantic segmentation methods are essential. However, distinguishing between the sea and the sky is challenging due to the differences between port and maritime environments. In this study, we built a dataset that captured perspectives from USVs and unmanned aerial vehicles in a maritime port environment and analysed the data features. Statistical analysis revealed a high correlation between the distribution of the sea and sky and row positional information. Based on this finding, a three-branch semantic segmentation network with a row position encoding module (RPEM) was proposed to improve the prediction accuracy between the sea and the sky. The proposed RPEM highlights the effect of row coordinates on feature extraction. Compared to the baseline, the three-branch network with RPEM significantly improved the ability to distinguish between the sea and the sky without significantly reducing the computational speed.
A Dual-Branch Framework for Semantic Change Detection with Boundary and Temporal Awareness
School of Information Science and Technology, Southwest Jiaotong University（信息科学与技术学院，西南交通大学）；School of Computer Science and Technology, Xidian University（计算机科学与技术学院，西安电子科技大学）
NavEYE: Vision-Centered Multi-Sensor Fusion-Based Situational Awareness System for Intelligent Surface Vehicles
Sanya Science and Education Innovation Park, Wuhan University of Technology（武汉理工大学三亚科教创新园）；School of Navigation, Wuhan University of Technology（武汉理工大学航运学院）；State Key Laboratory of Maritime Technology and Safety（船舶技术与安全国家重点实验室）
Panoptic Lintention Network: Towards Efficient Navigational Perception for the Visually Impaired
Institute for Anthropomatics and Robotics, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院人机与机器人研究所）；Sino-German School, Tongji University（同济大学中德学院）；Hangzhou SurImage Technology Company Ltd.（杭州 SurImage 科技有限公司）；Hangzhou KrVision Technology Company Ltd.（杭州 KrVision 科技有限公司）
Dynamic Topology Awareness: Breaking the Granularity Rigidity in Vision-Language Navigation
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空航天信息研究所）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）；Department of Computer Science, City University of Hong Kong（香港城市大学计算机科学系）
Autonomous Navigation of Cloud-Controlled Quadcopters in Confined Spaces Using Multi-Modal Perception and LLM-Driven High Semantic Reasoning
Department of Mechanical Engineering（机械工程系）；Rajshahi University of Engineering and Technology（拉贾沙希工程与技术大学）；Department of Industrial and Production Engineering（工业与生产工程系）；Shahjalal University of Science and Technology（沙赫jalal科学与技术大学）；Bangladesh University of Engineering and Technology（孟加拉工程与技术大学）；Department of Urban and Regional Planning（城市与区域规划系）；Department of Computer Science Engineering（计算机科学与工程系）；United International University（联合国际大学）
SpaAct: Spatially-Activated Transition Learning with Curriculum Adaptation for Vision-Language Navigation
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence（人机混合增强智能国家重点实验室）；National Engineering Research Center for Visual Information and Applications（视觉信息与应用国家工程研究中心）；Institute of Artificial Intelligence and Robotics（人工智能与机器人研究所）；The State Key Laboratory of Internet of Things for Smart City（智能城市物联网国家重点实验室）；Centre for Artificial Intelligence and Robotics（人工智能与机器人中心）；University of Macau（澳门大学）；Xiaomi EV（小i EV）；School of Automation（自动化学院）；Beijing Institute of Technology（北京理工大学）；Institute of Automation（自动化研究所）；Chinese Academy of Sciences（中国科学院）
Pedestrian-Robot Interactions on Autonomous Crowd Navigation: Reactive Control Methods and Evaluation Metrics
Swiss Federal School of Technology in Lausanne - EPFL（洛桑联邦理工学院）；University of Tsukuba（筑波大学）；Swiss Federal School of Technology in Zurich - ETH Zurich（苏黎世联邦理工学院）
