Sensor data integration algorithm for state estimation of autonomous robots in an intelligent transport system

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Abstract

This paper deals with the development and experimental testing of a sensor data integration algorithm for estimating the state of autonomous objects in intelligent transport systems (ITS). The main attention is paid to ensuring accuracy and reliability of navigation in complex and dynamically changing conditions of urban environment, where traditional navigation methods, such as GPS, may be insufficiently effective. The proposed algorithm combines data from different sensors (LIDAR, cameras, inertial sensors, GPS) and ITS elements to provide accurate position and trajectory estimation of autonomous systems. Experimental results obtained in simulation and field tests confirmed high accuracy and adaptability of the algorithm, which makes it promising for application in autonomous vehicles. The paper also discusses the possibilities of further development of machine learning algorithms and data protection methods to improve the efficiency and safety of navigation systems in ITS.

About the authors

Peter Mikhaylovich Trefilov

V.A. Trapeznikov Institute of Control Sciences of RAS

Email: petertrfi@ipu.ru
Moscow

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