1 research outputs found
DeepLocalization: Landmark-based Self-Localization with Deep Neural Networks
We address the problem of vehicle self-localization from multi-modal sensor
information and a reference map. The map is generated off-line by extracting
landmarks from the vehicle's field of view, while the measurements are
collected similarly on the fly. Our goal is to determine the autonomous
vehicle's pose from the landmark measurements and map landmarks. To learn this
mapping, we propose DeepLocalization, a deep neural network that regresses the
vehicle's translation and rotation parameters from unordered and dynamic input
landmarks. The proposed network architecture is robust to changes of the
dynamic environment and can cope with a small number of extracted landmarks.
During the training process we rely on synthetically generated ground-truth. In
our experiments, we evaluate two inference approaches in real-world scenarios.
We show that DeepLocalization can be combined with regular GPS signals and
filtering algorithms such as the extended Kalman filter. Our approach achieves
state-of-the-art accuracy and is about ten times faster than the related work.Comment: Accepted for publication by the IEEE Intelligent Transportation
Systems Conference (ITSC 2019), Auckland, New Zealan