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.

翻译：语义地图通过一组具有语义意义的物体对环境进行表征。这种表示方式具有存储高效、歧义性低、信息量更丰富等特点，从而有助于在高度非结构化、无GPS环境中实现大规模自主导航与可执行信息的获取。本文提出一种集成系统，能够在极具挑战性的冠层下方环境中执行大规模自主飞行与实时语义建图。我们利用激光雷达数据检测并建模树干与地面平面，并通过跨扫描数据关联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，以计算和存储高效的方式生成动态可行轨迹，引导无人机构建用户定义感兴趣区域的语义地图。同时设计漂移补偿机制，利用语义SLAM输出实时最小化里程计漂移，并保持规划器最优性与控制器稳定性，使无人机能够在大规模场景中精确、安全地执行任务。