Autonomous vehicles are complex robotic and artificial intelligence systems working together to achieve safe operation in unstructured environments. The objective of this work is to provide a foundation to develop more advanced algorithms for off-road autonomy. The project explores the sensors used for off-road autonomy and the data capture process. Additionally, the point cloud data captured from lidar sensors is processed to restore some of the geometric information lost during sensor sampling. Because ground truth values are needed for quantitative comparison, the MAVS was leveraged to generate a large off-road dataset in a variety of ecosystems. The results demonstrate data capture from the sensor suite and successful reconstruction of the selected geometric information. Using this geometric information, the point cloud data is more accurately segmented using the SqueezeSeg network.
|Advisor:||Ball, John E|
|Commitee:||Gurbuz, Ali, Dabbiru, Lalitha|
|School:||Mississippi State University|
|Department:||Electrical and Computer Engineering|
|School Location:||United States -- Mississippi|
|Source:||MAI 81/11(E), Masters Abstracts International|
|Subjects:||Computer Engineering, Artificial intelligence, Robotics|
|Keywords:||Autonomous vehicles, Deep learning, Lidar, MAVS, Point clouds, Squeezeseg|
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