Abstract: In this paper, propose method that can user’s position
that based on database is built from single camera. Previous
positioning calculate distance by arrival-time of signal like GPS
(Global Positioning System), RF(Radio Frequency). However, these
previous method have weakness because these have large error range
according to signal interference. Method for solution estimate position
by camera sensor. But, signal camera is difficult to obtain relative
position data and stereo camera is difficult to provide real-time
position data because of a lot of image data, too. First of all, in this
research we build image database at space that able to provide
positioning service with single camera. Next, we judge similarity
through image matching of database image and transmission image
from user. Finally, we decide position of user through position of most
similar database image. For verification of propose method, we
experiment at real-environment like indoor and outdoor. Propose
method is wide positioning range and this method can verify not only
position of user but also direction.
Abstract: In this paper, we propose a method that allows faster and more accurate detection of traffic lights by a vision sensor during driving, DGPS is used to obtain physical location of a traffic light, extract from the image information of the vision sensor only the traffic light area at this location and ascertain if the sign is in operation and determine its form. This method can solve the problem in existing research where low visibility at night or reflection under bright light makes it difficult to recognize the form of traffic light, thus making driving unstable. We compared our success rate of traffic light recognition in day and night road environments. Compared to previous researches, it showed similar performance during the day but 50% improvement at night.
Abstract: We propose obstacle classification method based on 2D
LIDAR Database. The existing obstacle classification method based
on 2D LIDAR, has an advantage in terms of accuracy and shorter
calculation time. However, it was difficult to classifier the type of
obstacle and therefore accurate path planning was not possible. In
order to overcome this problem, a method of classifying obstacle type
based on width data of obstacle was proposed. However, width data
was not sufficient to improve accuracy. In this paper, database was
established by width and intensity data; the first classification was
processed by the width data; the second classification was processed
by the intensity data; classification was processed by comparing to
database; result of obstacle classification was determined by finding
the one with highest similarity values. An experiment using an actual
autonomous vehicle under real environment shows that calculation
time declined in comparison to 3D LIDAR and it was possible to
classify obstacle using single 2D LIDAR.