The popularity of autonomous flight systems continues to grow as research demonstrates an increasingly broad range of applications with a spectrum of technologies that diminish the need for specialized equipment. Reducing reliance on expensive hardware and decreasing redundancy in sensors is made possible by leveraging the growing compute power of embedded systems and by the adoption of increasingly complex algorithms. Supporting architectures and software systems can increase feasibility by providing safe and robust development environments while increasing system confidence by providing diverse simulation environments for testing flight systems that have been built on new technologies. This work identifies two such categorical technologies, SLAM and machine learning, and surveys contemporary research concerning their use in assisting autonomous flight systems content with foreign objects. Additionally, this work presents a survey of supporting software used in such systems and demonstrates the integration of SLAM and machine learning through simulated environments.
|Commitee:||Elkaim, Gabriel, Dowla, Farid|
|School:||University of California, Santa Cruz|
|School Location:||United States -- California|
|Source:||MAI 82/8(E), Masters Abstracts International|
|Keywords:||Autonomous Flight, Drones, Machine Learning, Object Detection, Quadcopters, SLAM|
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