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dc.contributor.authorYuen, Wei Cheeen_US
dc.identifier.citationYuen, W. C. (2022). Vision assisted object detection In LIDAR point cloud. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractIn the age of Industry 4.0, the usage of autonomous guided robots has become a commonplace, especially in logistics, transport and manufacturing. With more labor-intensive operations being highly automated as a solution to improve the efficiency of manufacturing processes, autonomous guided vehicles (AGVs) are deployed to facilitate transportation of materials and finished products. The key to success is to develop a robust and secure avoidance policy for robots, therefore ensuring the safe maneuverability of the robot. The usage of a 2D LiDAR only for object collision avoidance results in frequent stop in navigation due to lack of target identification. Therefore, this project focuses on the training of a lightweight object detection model and the alignment of a LiDAR point cloud with the object detection model based on the live video footage from the RGB camera to provide a vision assisted object detection to enable the AGV to navigate and avoid obstacles in a dynamic environment. It is able to continuously track the target object , while navigating through a dynamic environment, avoiding obstacles.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleVision assisted object detection In LIDAR point clouden_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorXie Lihuaen_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeBachelor of Engineering (Electrical and Electronic Engineering)en_US
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Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)
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