ITSC 2025 Paper Abstract

Paper WE-EA-T13.4

Tran, Nguyen Hoang Khoi (The University of Sydney), Berrio Perez, Julie Stephany (University of Sydney), Shan, Mao (University of Sydney), Ming, Zhenxing (The University of Sydney), Worrall, Stewart (University of Sydney)

InterLoc: LiDAR-Based Intersection Localization Using Road Segmentation with Automated Evaluation Method

Scheduled for presentation during the Regular Session "S13b-Localization, Mapping, and Sensing for Robust Navigation in ITS" (WE-EA-T13), Wednesday, November 19, 2025, 14:30−14:50, Stradbroke

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on October 19, 2025

Keywords Sensor Integration and Calibration for Accurate Localization in Dynamic Road Conditions, Lidar-based Mapping and Environmental Perception for ITS Applications, Advanced Sensor Fusion for Robust Autonomous Vehicle Perception

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.