LiDAR Object Detection and - Sensor Fusion in Simulation Environments Sensor modelling towards advancements in Real2Sim - Sim2Real
Date
Type
Examensarbete på kandidatnivå
Programme
Model builders
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
In the world of autonomous driving the environment perception is of the outmost
importance. Light detection and ranging(LiDAR) data is one of the most common
sensor data in the field of autonomous driving. This thesis will therefore explore
LiDAR data as the first steps toward using it in a simulation environment. In order
to be able to explore the use of LiDAR, an arsenal of tools and knowledge is needed.
This thesis goes through the learning steps of these tools and in which way they fit
together to arrive at the result. This thesis includes different approaches of visualizing data using Robotic Operation Systems (ROS) framework and implementing 3D
bounding box annotation and also covers creating a convolutional auto encoder and
implementing it as a deep neural network. The result is knowledge on how LiDAR
data works, how it can be used and estimating some characteristics using a deep
neural network. Included in the results are a few visualizations of the data after
being trained on a neural network. Further work once this knowledge is gathered
would be to implement it into a simulation environment.
Description
Keywords
Sensor modelling, LiDAR, Sensor data visualization, Velodyne point, Bounding box, Autonomous driving, ROS, RVIZ, Deep neural network