Name: Lauro Jose Lyrio Junior
Type: MSc dissertation
Publication date: 25/08/2014

Namesort descending Role
Alberto Ferreira De Souza Advisor *
Thiago Oliveira dos Santos Co-advisor *

Examining board:

Namesort descending Role
Alberto Ferreira De Souza Advisor *
Claudine Santos Badue Internal Examiner *
Marley Maria Bernardes Rebuzzi Vellasco External Examiner *
Thiago Oliveira dos Santos Co advisor *

Summary: Mapping and localization are fundamental problems in autonomous robotics.
Autonomous robots need to know WHERE they are in their operational area to
navigate through it and to perform activities of interest. In this work, we
present an image-based mapping and localization system that employs Virtual
Generalizing Random Access Memory Weightless Neural Networks (VGRAM
WNN) for localizing an autonomous car.
In our system, a VG-RAM WNN learns world positions associated with
images and three-dimensional landmarks captured along a trajectory, in order
to build a map of the environment. During the localization, the system uses its
previous knowledge and uses an Extended Kalman Filter (EKF) to integrate
sensor data over time through consecutive steps of state prediction and
correction. The state prediction step is computed by means of our robot’s
motion model, which uses velocity and steering angle information computed
from images using visual odometry. The state correction step is performed by
integrating the VG-RAM WNN learned world positions in combination to the
matching of landmarks previously stored in the robot’s map. Our system
efficiently solves the (i) mapping, (ii) global localization and (iii) position
tracking problems using only camera images.
We performed experiments with our system using real-world datasets,
which were systematically acquired during laps around the Universidade
Federal do Espírito Santo (UFES) main campus (a 3.57 km long circuit). Our
experimental results show that the system is able to learn large maps (several
kilometres in length) of real world environments and perform global and
position tracking localization with mean pose precision of about 0.2m
compared to the Monte Carlo Localization (MCL) approach employed in our
autonomous vehicle.

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