Simultaneous Localization and Mapping (SLAM) enables autonomous robots to navigate and execute their tasks through unknown environments. However, performing SLAM in large environments with a single robot is not efficient, and visual or LiDAR-based SLAM requires feature extraction and matching algorithms, which are computationally expensive. In this paper, we present a collaborative SLAM approach with multiple robots using the pervasive WiFi radio signals. A centralized solution is proposed to optimize the trajectory based on the odometry and radio fingerprints collected from multiple robots. To improve the localization accuracy, a novel similarity model is introduced that combines received signal strength (RSS) and detection likelihood of an access point (AP). We perform extensive experiments to demonstrate the effectiveness of the proposed similarity model and collaborative SLAM framework.

翻译：同时定位和绘图(SLAM)使自主机器人能够在未知的环境中导航和执行任务,然而,在大型环境中使用单一机器人执行SLAM效率不高,视觉或LiDAR的SLAM需要地貌提取和匹配算法,这些算法在计算上非常昂贵。在本文中,我们展示了利用无线通信无线电信号与多个机器人合作的SLAM方法。提议采用集中化解决方案,优化基于从多个机器人收集到的odo测定和无线电指纹的轨迹。为了提高本地化的准确性,我们引入了一种新型相似性模型,将接收信号强度(RSS)和检测进入点的可能性(AP)结合起来。我们进行了广泛的实验,以证明拟议的类似模型和协作性SLM框架的有效性。