InterLoc: LiDAR-based Intersection Localization using Road Segmentation with Automated Evaluation Method

TL;DR

This paper introduces InterLoc, a LiDAR-based method for real-time intersection localization that leverages semantic road segmentation and an automated evaluation pipeline, improving accuracy and robustness over existing approaches.

Contribution

The paper presents a novel LiDAR-based intersection localization technique that utilizes semantic road scans and an automated OSM matching pipeline, addressing limitations of prior methods.

Findings

Outperforms recent learning-based baseline in accuracy and reliability.

Demonstrates robustness to segmentation errors in real-world scenarios.

Effective on SemanticKITTI dataset with real-time applicability.

Abstract

Online localization of road intersections is beneficial for autonomous vehicle localization, mapping and motion planning. Intersections offer strong landmarks for correcting vehicle pose estimation, anchoring new sensor data in up-to-date maps, and guiding vehicle routing in road network graphs. Despite this importance, intersection localization has not been widely studied, with existing methods either ignoring the rich semantic information already computed onboard or relying on scarce, hand-labeled intersection datasets. To close this gap, we present a novel LiDAR-based method for online vehicle-centric intersection localization. We detect the intersection candidates in a bird's eye view (BEV) representation formed by concatenating a sequence of semantic road scans. We then refine these candidates by analyzing the intersecting road branches and adjusting the intersection center point in a least-squares formulation. For evaluation, we introduce an automated pipeline that pairs localized intersection points with OpenStreetMap (OSM) intersection nodes using precise GNSS/INS ground-truth poses. Experiments on the SemanticKITTI dataset show that our method outperforms the latest learning-based baseline in accuracy and reliability. Sensitivity tests demonstrate the method's robustness to challenging segmentation errors, highlighting its applicability in the real world.