This paper introduces a comprehensive planning and navigation framework that address these limitations by integrating semantic mapping, adaptive coverage planning, dynamic obstacle avoidance and precise trajectory tracking. Our framework begins by generating panoptic occupancy local semantic maps and accurate localization information from data aligned between a monocular camera, IMU, and GPS. This information is combined with input terrain point clouds or preloaded terrain information to initialize the planning process. We propose the Radiant Field-Informed Coverage Planning algorithm, which utilizes a diffusion field model to dynamically adjust the robot's coverage trajectory and speed based on environmental attributes such as dirtiness and dryness. By modeling the spatial influence of the robot's actions using a Gaussian field, ensures a speed-optimized, uniform coverage trajectory while adapting to varying environmental conditions.

翻译：本文提出了一种综合规划与导航框架，通过集成语义建图、自适应覆盖规划、动态避障和精确轨迹跟踪来解决这些局限性。我们的框架首先通过单目相机、IMU和GPS之间的对齐数据生成全景占据局部语义地图及精确定位信息。该信息与输入的地形点云或预载地形信息相结合，以初始化规划过程。我们提出了辐射场感知覆盖规划算法，该算法利用扩散场模型，根据污浊度、干燥度等环境属性动态调整机器人的覆盖轨迹与速度。通过使用高斯场对机器人动作的空间影响进行建模，该算法在适应多变环境条件的同时，确保了速度最优化的均匀覆盖轨迹。