This paper presents a novel approach for autonomous cooperating UAVs in search and rescue operations in subterranean domains with complex topology. The proposed system was ranked second in the Virtual Track of the DARPA SubT Finals as part of the team CTU-CRAS-NORLAB. In contrast to the winning solution that was developed specifically for the Virtual Track, the proposed solution also proved to be a robust system for deployment onboard physical UAVs flying in the extremely harsh and confined environment of the real-world competition. The proposed approach enables fully autonomous and decentralized deployment of a UAV team with seamless simulation-to-world transfer, and proves its advantage over less mobile UGV teams in the flyable space of diverse environments. The main contributions of the paper are present in the mapping and navigation pipelines. The mapping approach employs novel map representations -- SphereMap for efficient risk-aware long-distance planning, FacetMap for surface coverage, and the compressed topological-volumetric LTVMap for allowing multi-robot cooperation under low-bandwidth communication. These representations are used in navigation together with novel methods for visibility-constrained informed search in a general 3D environment with no assumptions about the environment structure, while balancing deep exploration with sensor-coverage exploitation. The proposed solution also includes a visual-perception pipeline for on-board detection and localization of objects of interest in four RGB stream at 5 Hz each without a dedicated GPU. Apart from participation in the DARPA SubT, the performance of the UAV system is supported by extensive experimental verification in diverse environments with both qualitative and quantitative evaluation.

翻译：本文提出了一种适用于复杂拓扑地下环境搜救任务的新型自主协作无人机方法。该系统作为CTU-CRAS-NORLAB团队的一部分，在DARPA SubT决赛虚拟赛道中获得了第二名。与专门为虚拟赛道开发的优胜方案不同，本方案还被证明是能够在极端恶劣、空间受限的真实竞赛环境中部署于物理无人机的鲁棒系统。所提出的方法实现了完全自主且去中心化的无人机编队部署，具备无缝的仿真到现实迁移能力，并在多种环境中的可飞行空间内证明了其相对于机动性较低的地面机器人团队的优势。本文的主要贡献在于建图与导航管线。建图方法采用了新颖的地图表示：SphereMap用于高效的风险感知长距离规划，FacetMap用于表面覆盖，以及压缩的拓扑-体积混合地图LTVMap用于在低带宽通信下实现多机器人协作。这些地图表示与新型方法相结合，用于在无环境结构假设的一般三维环境中，平衡深度探索与传感器覆盖利用，同时实现受可见性约束的启发式搜索。所提出的方案还包括一个视觉感知管线，可在无专用GPU的情况下，以每路5 Hz的频率处理四路RGB视频流，实现机载目标检测与定位。除参与DARPA SubT竞赛外，该无人机系统的性能还通过多种环境中的大量实验验证得到了支持，并进行了定性与定量评估。