We present an open-source system for Micro-Aerial Vehicle autonomous navigation from vision-based sensing. Our system focuses on dense mapping, safe local planning, and global trajectory generation, especially when using narrow field of view sensors in very cluttered environments. In addition, details about other necessary parts of the system and special considerations for applications in real-world scenarios are presented. We focus our experiments on evaluating global planning, path smoothing, and local planning methods on real maps made on MAVs in realistic search and rescue and industrial inspection scenarios. We also perform thousands of simulations in cluttered synthetic environments, and finally validate the complete system in real-world experiments.
Mohta al @cite_23 also propose an autonomous system for fast UAV flight through cluttered environments. There are a few key differences with their work, especially on mapping and planning. They use a LIDAR as the main sensor, which gives 360 @math field of view for collision detection, removing many of the issues with narrow field of view sensors which we attempt to address in this work. They also only keep a small local 3D map, and use a global 2D map to escape local minima, whereas we use a full global 3D approach at comparable computation speeds. For how the mapping is used, they attempt to break the world into overlapping convex free-space regions, which grows in complexity and is increasingly more limited as the space gets more complex, while we always plan directly in the map space. They also make no considerations for how drift will affect the map other than to keep only a local 3D map.
@cite_23
2771926486
Author(s): Mohta, K.; Mulgaonkar, Y.; Watterson, M.; Liu, S.; Qu, C.; Makineni, A.; Saulnier, K.; Sun, K.; Zhu, A.; Delmerico, J.; Karydis, K.; Atanasov, N.; Loianno, G.; Scaramuzza, D.; Daniilidis, K.; Taylor, C. J.; Kumar, V.