UAV- Navigation Using Map Slicing and Safe Path Computation

Halil Utku Unlu, Dimitris Chaikalis, Athanasios Tsoukalas, A. Tzes
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Abstract

This paper is concerned with the safe path planning of a drone while exploring an unknown space. The drone is localized by fusing measurements from sensors including an IMU, RGB-D sensor, and an optical flow system, while executing an RTAB-Map SLAM algorithm. The 3D-occupancy Octomap is generated online and a slicing algorithm is employed to compute 2D-maps. The maps' traversible coordinates are identified and used as potential points for the drone intermediate navigation to the destination. The final segment corresponds to a shortest Chebyshev-length path between all frontier pixels and the endpoint over the unexplored map region. The drone's path is computed using a skeletal path between the identified map boundaries so that the drone moves from its current location through the free map coordinates to the destination point. Simulation studies using within the interior of an apartment indicate the efficiency and effectiveness of the proposed method.
基于地图切片和安全路径计算的无人机导航
本文研究了无人机在探索未知空间时的安全路径规划问题。无人机通过融合来自包括IMU、RGB-D传感器和光流系统在内的传感器的测量来定位,同时执行rtaba - map SLAM算法。在线生成三维占用八层地图,并采用切片算法计算二维地图。地图的可穿越坐标被识别并用作无人机到目的地的中间导航的潜在点。最后一段对应于所有边界像素和未探测地图区域上的端点之间最短的切比雪夫长度路径。无人机的路径是使用识别地图边界之间的骨架路径计算的,这样无人机就可以通过自由地图坐标从当前位置移动到目的地。在公寓内部进行的仿真研究表明了该方法的效率和有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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