Range-SLAM: Ultra-Wideband-Based Smoke-Resistant Real-Time Localization and Mapping

Abstract

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

Figures (10)

• Figure 1: Range-SLAM enables robots to perform environment mapping and localization at 50Hz in smoke-filled environments.
• Figure 2: From left to right: the "NLOS Identification" module \ref{['subsec:NLOS Identification']} performs UWB data (50Hz) extraction, including distance measurement, RxRssi, and FpRssi, followed by preprocessing steps such as normalization, exception handling, and smoothing. An SVM classifier is applied to identify the LOS condition. Subsequently, the "Mapping" module \ref{['subsec:Mapping Algorithm']} utilizes a raycasting algorithm to continuously update the grid map. Finally, the "Location" module \ref{['subsec:Location Algorithm']} use the WLS method to estimate the tag's position based on the grid map.
• Figure 3: The NLOS identification accuracy comparison within the different ranges for seven obstacles. In each bar chart, the y-axis represents the recognition accuracy rate, while the x-axis denotes the distance between the tag and anchor. To address NLOS identification, we use an SVM classifier for real-time binary classification, achieving near-perfect NLOS recognition for iron materials and human bodies with significant positioning interference, some recognition rate is sacrificed for materials like plastic and foam yet.
• Figure 4: Illustration of Range-SLAM mapping and localization enhancement. (a)Scene Description (b)Free-Occupied Mapping Algorithm (c)Motion Real-time Mapping (d)UWB Prior Map for Localization Enhancement
• Figure 5: Our Range-SLAM mobile platform and real scenarios for the experiment.