  This paper introduces a visual-based localization method for autonomous
vehicles (AVs) that operate in the absence of any complicated hardware system
but a single camera. Visual localization refers to techniques that aim to find
the location of an object based on visual information of its surrounding area.
The problem of localization has been of interest for many years. However,
visual localization is a relatively new subject in the literature of
transportation. Moreover, the inevitable application of this type of
localization in the context of autonomous vehicles demands special attention
from the transportation community to this problem. This study proposes a
two-step localization method that requires a database of geotagged images and a
camera mounted on a vehicle that can take pictures while the car is moving. The
first step which is image retrieval uses SIFT local feature descriptor to find
an initial location for the vehicle using image matching. The next step is to
utilize the Kalman filter to estimate a more accurate location for the vehicle
as it is moving. All stages of the introduced method are implemented as a
complete system using different Python libraries. The proposed system is tested
on the KITTI dataset and has shown an average accuracy of 2 meters in finding
the final location of the vehicle.
