Mapping and obstacle avoidance algorithms for quadrotors in the indoor environments

Oral, Ömer
Recently, there has been increased interest for search and rescue missions with autonomous flying vehicles. However, as most of the designed techniques are suitable for outdoors, only a few techniques have been developed for indoors. SLAM (Simultaneous Localization and Mapping) is a method that allows autonomous robots to navigate in both indoor and outdoor environments. Localization part can be easily performed using a GPS(Global Positioning System) outdoors. On the contrary, GPS cannot be used indoors. In this study, the aim is to obtain 2D map of indoor environments without hitting any obstacles by using a quadrotor that is capable of autonomous navigation. Local positioning system is established with a UWB(Ultra Wide-Band) sensor and a LIDAR(Laser Imaging Detection and Ranging) is used to obtain the map of the unknown indoor environment. A novel algorithm, which maps indoor environments autonomously, is designed and presented. It is compared with two known navigation algorithms with the help of various metrics in order to measure the performance of the presented algorithm. One of the known algorithms directly fails on bigger maps with obstacles while the other one is overtaken by the presented novel algorithm during comparisons although it successfully completes the mapping process. The algorithms have obtained similar results in some simulations on small map. However, the novel algorithm beats the opponents by completing the tasks with better scores regardless of the size of the indoor environments.


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Citation Formats
Ö. Oral, “Mapping and obstacle avoidance algorithms for quadrotors in the indoor environments,” Thesis (M.S.) -- Graduate School of Natural and Applied Sciences. Mechanical Engineering., Middle East Technical University, 2019.