# DeepLocalization: Landmark-based Self-Localization with Deep Neural Networks

## TL;DR

DeepLocalization employs deep neural networks to accurately and efficiently determine vehicle pose from landmark data, demonstrating robustness to environmental changes and outperforming existing methods in speed and accuracy.

## Contribution

This work introduces a novel neural network architecture for landmark-based vehicle self-localization that is robust, fast, and capable of integrating with GPS and filtering algorithms.

## Key findings

- Achieves state-of-the-art localization accuracy.
- Operates approximately ten times faster than previous methods.
- Effectively handles dynamic environments with few landmarks.

## Abstract

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.