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Paper WeAT1.3

Quack, Tobias (RWTH Aachen University), Hesseler, Frank-Josef (RWTH Aachen University, Institute of Automatic Control), Abel, Dirk (RWTH-Aachen University)

Fast Real-Time Localization with Sparse Digital Maps for Connected Automated Vehicles in Urban Areas

Scheduled for presentation during the Regular Session "Guidance and Collision Avoidance" (WeAT1), Wednesday, June 26, 2019, 11:10−11:30, Chambord

9th IFAC International Symposium on Advances in Automotive Control, June 23-27, 2019, Orléans, France

This information is tentative and subject to change. Compiled on March 28, 2024

Keywords Autonomous Driving and Collision Avoidance: Sensor Fusion, Modeling of the Environment, Control Architectures, Intelligent Vehicles and Robotics Technology in Vehicles

Abstract

For the realization of advanced automation systems in road vehicles, accurate and robust localization is a crucial requirement. Satellite-based systems such as GPS are generally able to provide geolocations, but their precision and robustness can be impaired strongly due to shading of satellites and multipath effects, especially in urban surroundings. Localization methods based on environment perception sensors and digital maps are therefore widely used in the field of autonomous vehicles with accuracy, robustness, real-time capability and sparseness of the maps being major objectives. In this paper, we present a fast, real-time capable implementation of a Monte Carlo Localization scheme which operates on a storage space efficient digital map and is targeted to provide precise localization in urban surroundings at a rate of 50 Hz. For the experimental evaluation, we use our test vehicle's LiDAR sensor combined with wheel odometry, inertial measurements and a low-cost GPS.

 

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