As mobile robots become more ubiquitous, their deployments grow across use cases where GNSS positioning is either unavailable or unreliable. This has led to increased interest in multi-modal relative localization methods. Complementing onboard odometry, ranging allows for relative state estimation, with ultra-wideband (UWB) ranging having gained widespread recognition due to its low cost and centimeter-level out-of-box accuracy. Infrastructure-free localization methods allow for more dynamic, ad-hoc, and flexible deployments, yet they have received less attention from the research community. In this work, we propose a cooperative relative multi-robot localization where we leverage inter-robot ranging and simultaneous spatial detections of objects in the environment. To achieve this, we equip robots with a single UWB transceiver and a stereo camera. We propose a novel Monte-Carlo approach to estimate relative states by either employing only UWB ranges or dynamically integrating simultaneous spatial detections from the stereo cameras. We also address the challenges for UWB ranging error mitigation, especially in non-line-of-sight, with a study on different LSTM networks to estimate the ranging error. The proposed approach has multiple benefits. First, we show that a single range is enough to estimate the accurate relative states of two robots when fusing odometry measurements. Second, our experiments also demonstrate that our approach surpasses traditional methods such as multilateration in terms of accuracy. Third, to increase accuracy even further, we allow for the integration of cooperative spatial detections. Finally, we show how ROS 2 and Zenoh can be integrated to build a scalable wireless communication solution for multi-robot systems. The experimental validation includes real-time deployment and autonomous navigation based on the relative positioning method.

翻译：随着移动机器人日益普及，其部署场景不断扩展至全球导航卫星系统（GNSS）定位不可用或不可靠的环境，这促使多模态相对定位方法受到广泛关注。作为机载里程计的补充，测距技术可实现相对状态估计，其中超宽带（UWB）测距因低成本及厘米级出厂精度而获得广泛认可。免基础设施的定位方法支持更具动态性、临时性与灵活性的部署，但尚未获得学界充分重视。本文提出一种协作式多机器人相对定位方法，利用机器人间测距与环境目标的同时空间检测。为此，我们为机器人配备单个UWB收发器与立体相机，并提出一种新颖的蒙特卡洛方法：通过仅使用UWB测距或动态融合立体相机的同步空间检测来估计相对状态。针对UWB测距误差抑制（尤其在非视距场景下）的挑战，我们研究了多种长短时记忆网络（LSTM）结构以估计测距误差。该方法具有多重优势：首先，实验表明融合里程计测量时单个测距值即可精确估计双机器人相对状态；其次，我们的方法在精度上超越传统多边定位法；此外，为进一步提升精度，我们支持集成协作式空间检测；最后，我们展示了如何整合ROS 2与Zenoh构建可扩展的多机器人无线通信方案。实验验证包含基于该相对定位方法的实时部署与自主导航。