Nadal, Noël (INRIA), Lasgouttes, Jean-Marc (INRIA), Nashashibi, Fawzi (INRIA)

Improving Landmark Positions in Urban Maps with Crowdsourced Data

Scheduled for presentation during the Regular Session "S43a-Multi-Sensor Fusion and Perception for Robust Autonomous Driving" (FR-LM-T43), Friday, November 21, 2025, 11:30−11:50, 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 18, 2025

Abstract

Precise localization is critical for the safety and efficiency of autonomous driving, where reliable navigation and decision-making depend on accurate geospatial data. A well-established technique to achieve decimeter-level precision is the use of landmarks, which requires highly accurate and frequently updated maps to ensure optimal performance. Although prior research has extensively explored the addition or removal of landmarks in maps, improving the positional accuracy of existing landmarks has been less addressed, with existing solutions often limited to validating landmark presence or absence. This study proposes a novel approach to refine the coordinates of existing landmarks using crowdsourced data, collected from sensors that may include potentially low-cost, low-precision devices. Our results demonstrate that even a few crowdsourced updates can significantly enhance landmark accuracy. These findings highlight the scalability and cost-effectiveness of crowdsourcing for map refinement, opening promising perspectives for low-cost, high-precision map updates in autonomous driving and other geospatial applications.