Semantic maps represent the environment using a set of semantically meaningful objects. This representation is storage-efficient, less ambiguous, and more informative, thus facilitating large-scale autonomy and the acquisition of actionable information in highly unstructured, GPS-denied environments. In this letter, we propose an integrated system that can perform large-scale autonomous flights and real-time semantic mapping in challenging under-canopy environments. We detect and model tree trunks and ground planes from LiDAR data, which are associated across scans and used to constrain robot poses as well as tree trunk models. The autonomous navigation module utilizes a multi-level planning and mapping framework and computes dynamically feasible trajectories that lead the UAV to build a semantic map of the user-defined region of interest in a computationally and storage efficient manner. A drift-compensation mechanism is designed to minimize the odometry drift using semantic SLAM outputs in real time, while maintaining planner optimality and controller stability. This leads the UAV to execute its mission accurately and safely at scale. Code is released at: https://github.com/KumarRobotics/kr_autonomous_flight and https://github.com/KumarRobotics/sloam.

翻译：语义地图利用一组语义有意义的对象来表示环境。这种表示方式存储效率高、歧义性低且信息更丰富，因此有助于在高度非结构化、无GPS信号的环境下实现大规模自主飞行与可操作信息的获取。本文提出了一种集成系统，可在具有挑战性的林冠下环境中执行大规模自主飞行与实时语义建图。我们通过激光雷达数据检测并建模树干与地面平面，这些特征在多次扫描间建立关联，并用于约束机器人位姿及树干模型。自主导航模块采用多级规划与建图框架，计算动态可行的轨迹，引导无人机以计算与存储高效的方式构建用户指定感兴趣区域的语义地图。设计了漂移补偿机制，利用语义SLAM输出实时最小化里程计漂移，同时保持规划器最优性与控制器稳定性，从而使无人机能够在大规模场景下准确、安全地执行任务。代码已开源：https://github.com/KumarRobotics/kr_autonomous_flight 和 https://github.com/KumarRobotics/sloam。