Analyzing the impact of semantic LoD3 building models on image-based vehicle localization

TL;DR

This paper explores how detailed semantic LoD3 building models improve image-based vehicle localization accuracy in urban environments, especially where GNSS signals are unreliable.

Contribution

It introduces a novel approach integrating semantic 3D building models with image features to enhance vehicle positioning accuracy, demonstrating the practical benefits of LoD3 models.

Findings

LoD3 models detect 69% more features than LoD2.

LoD3 models improve localization accuracy in urban canyons.

The approach combines off-the-shelf features and deep learning methods.

Abstract

Numerous navigation applications rely on data from global navigation satellite systems (GNSS), even though their accuracy is compromised in urban areas, posing a significant challenge, particularly for precise autonomous car localization. Extensive research has focused on enhancing localization accuracy by integrating various sensor types to address this issue. This paper introduces a novel approach for car localization, leveraging image features that correspond with highly detailed semantic 3D building models. The core concept involves augmenting positioning accuracy by incorporating prior geometric and semantic knowledge into calculations. The work assesses outcomes using Level of Detail 2 (LoD2) and Level of Detail 3 (LoD3) models, analyzing whether facade-enriched models yield superior accuracy. This comprehensive analysis encompasses diverse methods, including off-the-shelf feature matching and deep learning, facilitating thorough discussion. Our experiments corroborate that LoD3 enables detecting up to 69\% more features than using LoD2 models. We believe that this study will contribute to the research of enhancing positioning accuracy in GNSS-denied urban canyons. It also shows a practical application of under-explored LoD3 building models on map-based car positioning.