Navigation in complex 3D scenarios requires appropriate environment representation for efficient scene understanding and trajectory generation. We propose a highly efficient and extensible global navigation framework based on a tomographic understanding of the environment to navigate ground robots in multi-layer structures. Our approach generates tomogram slices using the point cloud map to encode the geometric structure as ground and ceiling elevations. Then it evaluates the scene traversability considering the robot's motion capabilities. Both the tomogram construction and the scene evaluation are accelerated through parallel computation. Our approach further alleviates the trajectory generation complexity compared with planning in 3D spaces directly. It generates 3D trajectories by searching through multiple tomogram slices and separately adjusts the robot height to avoid overhangs. We evaluate our framework in various simulation scenarios and further test it in the real world on a quadrupedal robot. Our approach reduces the scene evaluation time by 3 orders of magnitude and improves the path planning speed by 3 times compared with existing approaches, demonstrating highly efficient global navigation in various complex 3D environments. The code is available at: https://github.com/byangw/PCT_planner.

翻译：在复杂三维场景的导航中，需要合适的环境表示以实现高效的场景理解与轨迹生成。我们提出了一种基于环境层析理解的高效可扩展全局导航框架，用于引导地面机器人在多层结构中完成导航。该方法通过点云地图生成层析切片，将几何结构编码为地面与天花板高程信息，并结合机器人运动能力评估场景可通行性。层析切片构建与场景评估均通过并行计算加速。与直接在三维空间中进行规划相比，该方法进一步降低了轨迹生成的复杂度：通过搜索多个层析切片生成三维轨迹，并分别调整机器人高度以避免悬空障碍物。我们在多种仿真场景中评估该框架，并在真实世界的四足机器人上完成了测试。相比现有方法，该方法将场景评估时间降低了三个数量级，路径规划速度提升了三倍，展示了在多种复杂三维环境中高效全局导航的能力。代码已开源：https://github.com/byangw/PCT_planner。