Monocular Camera Localization for Automated Vehicles Using Image Retrieval

TL;DR

This paper presents a real-time monocular camera localization method for autonomous vehicles that combines image retrieval, mapping, and particle filtering, achieving 10cm accuracy with lower computational requirements.

Contribution

It introduces a scalable, efficient localization approach that does not rely on LiDARs or HD maps, using adapted algorithms in image retrieval and particle filtering.

Findings

Achieves real-time localization with 10cm accuracy.

Comparable performance to LiDAR-based monocular methods.

Demonstrates effectiveness in both outdoor and indoor tests.

Abstract

We address the problem of finding the current position and heading angle of an autonomous vehicle in real-time using a single camera. Compared to methods which require LiDARs and high definition (HD) 3D maps in real-time, the proposed approach is easily scalable and computationally efficient, at the price of lower precision. The new method combines and adapts existing algorithms in three different fields: image retrieval, mapping database, and particle filtering. The result is a simple, real-time localization method using an image retrieval method whose performance is comparable to other monocular camera localization methods which use a map built with LiDARs. We evaluate the proposed method using the KITTI odometry dataset and via closed-loop experiments with an indoor 1:10 autonomous vehicle. The tests demonstrate real-time capability and a 10cm level accuracy. Also, experimental results of the closed-loop indoor tests show the presence of a positive feedback loop between the localization error and the control error. Such phenomena is analysed in details at the end of the article.