Tipo di tesi
Tesi di laurea magistrale
Titolo
Small Obstacles Detection for Autonomous Navigation
Dipartimento
INGEGNERIA DELL'INFORMAZIONE
Corso di studi
INGEGNERIA ROBOTICA E DELL'AUTOMAZIONE
Riassunto (Italiano)
The work focuses on an obstacle detection framework for an autonomous robotics platform. State of the art classical and Deep Learning existing frameworks and approaches are first explored. A probabilistic framework that includes 3D reconstruction techniques, stereo matching and UV-disparity projection is designed. The system is integrated with appearance cues, edges related metrics and ground plane homography estimation to robustly segment obstacles in a wide range of scenarios. A synthetic and real datasets are generated to test the performances and accuracy of the proposed algorithms. The framework is deployed on a autonomous mobile robot where real time performances are achieved, using CUDA computing capabilities of a NVIDIA Xavier embedded platform.
