Date Approved
12-2018
Graduate Degree Type
Thesis
Degree Name
Engineering (M.S.E.)
Degree Program
School of Engineering
First Advisor
Dr. Nicholas Baine
Second Advisor
Dr. Bruce Dunne
Third Advisor
Dr. Samhita Rhodes
Academic Year
2018/2019
Abstract
The detection and tracking of objects around an autonomous vehicle is essential to operate safely. This paper presents an algorithm to detect, classify, and track objects. All objects are classified as moving or stationary as well as by type (e.g. vehicle, pedestrian, or other). The proposed approach uses state of the art deep-learning network YOLO (You Only Look Once) combined with data from a laser scanner to detect and classify the objects and estimate the position of objects around the car. The Oriented FAST and Rotated BRIEF (ORB) feature descriptor is used to match the same object from one image frame to another. This information fused with measurements from a coupled GPS/INS using an Extended Kalman Filter. The resultant solution aids in the localization of the car itself and the objects within its environment so that it can safely navigate the roads autonomously. The algorithm has been developed and tested using the dataset collected by Oxford Robotcar. The Robotcar is equipped with cameras, LiDAR, GPS and INS collected data traversing a route through the crowded urban environment of central Oxford.
ScholarWorks Citation
Aryal, Milan, "Object Detection, Classification, and Tracking for Autonomous Vehicle" (2018). Masters Theses. 912.
https://scholarworks.gvsu.edu/theses/912
