Real time object localization based on computer vision: Cone detection for perception module of a racing car for Formula student driverless
Typ
Examensarbete för masterexamen
Program
Systems, control and mechatronics (MPSYS), MSc
Publicerad
2020
Författare
Svecovs, Maksims
Hörnschemeyer, Felix
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Using a Formula student car to race autonomously and find the right path has
been around since the introduction of this class in the Formula student competition
in 2017. This project work attempts this problem using a pure computer vision
approach by utilizing a stereo vision camera only and running a convolutional neural
network to localize the object in a field of view. To make sure the approach works
at a stable high frequency, the detection of the cone-objects which are marking the
path is run on a GPU. As the output of the system, the two dimensional coordinates
in a local frame are calculated from each detection made in each frame, using merged
approaches of triangulation and distance by size in the frame. The result is a solution
with a sufficiently precise detection up to a distance around 12m, running at a
frequency of 25Hz with more than 12 detected objects per frame, the system proved
to be well enough designed to provide data for path planning module.
Beskrivning
Ämne/nyckelord
Convolutional neural network , GPU , TinyYOLOv3 , Formula student , autonomous driving , object detection