Name: Thiago Gonçalves Cavalcante
Type: MSc dissertation
Publication date: 11/05/2022
Advisor:

Namesort descending Role
Claudine Santos Badue Advisor *

Examining board:

Namesort descending Role
Avelino Forechi Silva Co advisor *
Claudine Santos Badue Advisor *
Denis Fernando Wolf External Examiner *
Renato Antônio Krohling Internal Examiner *

Summary: In this work, we present a visual global localization system based on Deep Neural Networks
(DNNs) for self-driving cars, called DeepVGL (Deep Visual Global Localization), which
receives real-time images from a forward-facing camera installed on car’s roof and infers their
corresponding position in global coordinates. To this end, DeepVGL is trained with pairs of
coordinates and associated images belonging to datasets of autonomous vehicles built with
sensor data aligned in time and space through a process of Simultaneous Localization And
Mapping (SLAM). To assess the performance of DeepVGL, we carried out experiments using
datasets composed of camera images collected by different self-driving cars on trips made over
long time spans (over 4 years), thus including significant changes in the environment, traffic
volume and weather conditions, as well as different times of the day and seasons of the year.
We also compared DeepVGL with a state-of-the-art global localization system based on WNN.
Finally, we executed experiments using datasets composed of LIDAR range images obtained
by a self-driving truck on trips made over reasonable time spans (over 3 months). The
experimental results show that DeepVGL can correctly estimate the global localization of the
self-driving car up to 75% of the time for an accuracy of 0.2 m and up to 96% of the time for
an accuracy of 5 m. The results also show that DeepVGL outperforms WNN, which can
correctly locate the self-driving car up to 76% of the time for 0.2 m accuracy, but only up to
89% of the time for 5 m accuracy. Finally, the results show that DeepVGL works better with
LIDAR range images than camera images, locating the autonomous truck up to 95% of the time
for 0.2 m accuracy and 98% of the time for 5 m accuracy.

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