Monocular Vehicle Self-localization method based on Compact Semantic Map

TL;DR

This paper introduces a monocular camera-based vehicle self-localization method utilizing a compact semantic map, achieving high accuracy with minimal data and sensor requirements in urban environments.

Contribution

The novel approach combines deep neural network landmark recognition with geometric feature extraction for precise localization using a minimal semantic map.

Findings

Achieved RMS accuracy of 0.345 meters in vehicle localization.

Reduced sensor setup and map storage compared to existing methods.

Validated effectiveness on an open dataset.

Abstract

High precision localization is a crucial requirement for the autonomous driving system. Traditional positioning methods have some limitations in providing stable and accurate vehicle poses, especially in an urban environment. Herein, we propose a novel self-localizing method using a monocular camera and a 3D compact semantic map. Pre-collected information of the road landmarks is stored in a self-defined map with a minimal amount of data. We recognize landmarks using a deep neural network, followed with a geometric feature extraction process which promotes the measurement accuracy. The vehicle location and posture are estimated by minimizing a self-defined re-projection residual error to evaluate the map-to-image registration, together with a robust association method. We validate the effectiveness of our approach by applying this method to localize a vehicle in an open dataset, achieving the RMS accuracy of 0.345 meter with reduced sensor setup and map storage compared to the state of art approaches. We also evaluate some key steps and discuss the contribution of the subsystems.