We present a multi-robot system for GPS-denied search and rescue under the forest canopy. Forests are particularly challenging environments for collaborative exploration and mapping, in large part due to the existence of severe perceptual aliasing which hinders reliable loop closure detection for mutual localization and map fusion. Our proposed system features unmanned aerial vehicles (UAVs) that perform onboard sensing, estimation, and planning. When communication is available, each UAV transmits compressed tree-based submaps to a central ground station for collaborative simultaneous localization and mapping (CSLAM). To overcome high measurement noise and perceptual aliasing, we use the local configuration of a group of trees as a distinctive feature for robust loop closure detection. Furthermore, we propose a novel procedure based on cycle consistent multiway matching to recover from incorrect pairwise data associations. The returned global data association is guaranteed to be cycle consistent, and is shown to improve both precision and recall compared with the input pairwise associations. The proposed multi-UAV system is validated both in simulation and during real-world collaborative exploration missions at NASA Langley Research Center.

中文翻译：

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多架无人机在林冠下搜救

我们提出了一个多机器人系统，用于在森林树冠下进行 GPS 拒绝搜索和救援。森林对于协作探索和制图来说是特别具有挑战性的环境，这在很大程度上是由于存在严重的感知混叠，这阻碍了相互定位和地图融合的可靠回环检测。我们提出的系统具有执行机载传感、估计和规划的无人机 (UAV)。当通信可用时，每架无人机将压缩的基于树的子地图传输到中央地面站，以进行协作同步定位和制图 (CSLAM)。为了克服高测量噪声和感知混叠，我们使用一组树的局部配置作为稳健回环检测的独特特征。此外，我们提出了一种基于循环一致多路匹配的新程序，以从不正确的成对数据关联中恢复。返回的全局数据关联保证循环一致，并且与输入的成对关联相比，可以提高精度和召回率。拟议的多无人机系统在美国宇航局兰利研究中心的模拟和现实合作探索任务中都得到了验证。