Range-SLAM：基于超宽带的抗烟雾实时定位与建图
Range-SLAM: Ultra-Wideband-Based Smoke-Resistant Real-Time Localization and Mapping
AI总结：
针对低能见度恶劣环境下光学传感器SLAM失效的问题，提出基于通用UWB设备的轻量实时Range-SLAM系统，结合光线投射算法与WLS方法实现2D栅格建图与定位，已开展含烟雾环境的真实实验验证。
摘要：
本文提出了Range-SLAM，这是一种实时、轻量的SLAM（同时定位与建图）系统，旨在利用超宽带（Ultra-Wideband, UWB）信号解决烟雾及其他恶劣环境下的定位与建图难题。激光雷达、相机等光学传感器在低能见度环境中难以正常工作，而UWB信号为实时定位提供了可靠的替代方案。该系统使用通用UWB设备即可实现精准建图与定位，无需依赖昂贵的激光雷达或其他专用硬件。仅利用UWB传感器提供的与锚点相关的距离和接收信号强度指示（Received Signal Strength Indicator, RSSI），我们将携带标签的智能体的运动与光线投射（raycasting）算法相结合，实时构建二维占用栅格地图。为提升复杂条件下的定位效果，系统采用了加权最小二乘（Weighted Least Squares, WLS）方法。已开展大量真实世界实验，包括充满烟雾的环境和模拟
英文摘要：
This paper presents Range-SLAM, a real-time, lightweight SLAM system designed to address the challenges of localization and mapping in environments with smoke and other harsh conditions using Ultra-Wideband (UWB) signals. While optical sensors like LiDAR and cameras struggle in low-visibility environments, UWB signals provide a robust alternative for real-time positioning. The proposed system uses general UWB devices to achieve accurate mapping and localization without relying on expensive LiDAR or other dedicated hardware. By utilizing only the distance and Received Signal Strength Indicator (RSSI) provided by UWB sensors in relation to anchors, we combine the motion of the tag-carrying agent with raycasting algorithm to construct a 2D occupancy grid map in real time. To enhance localization in challenging conditions, a Weighted Least Squares (WLS) method is employed. Extensive real-world experiments, including smoke-filled environments and simulated
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
