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Decentralized Localization Framework using Heterogeneous Map-matchings

Highly accurate and robust real-time localization is an essential technique for various autonomous driving applications. Numerous localization methods have been proposed that combine various types of sensors, including an environmental sensor, IMU and GPS. However, the usage of a single environmental sensor is rather fragile. Although the use of multi-environment sensors is a better alternative, fusion methods from previous studies have not adequately compensated for shortcomings in dissimilar sensors or have not considered errors in the pre-built map. In this paper, we propose a decentralized localization framework using heterogeneous map-matching sources. Decentralized localization performs two independent map-matchings and integrates them with a stochastic situational analysis model. By applying a stochastic model, the reliability of the two map matchings is collected and system stability is verified. A number of experiments with autonomous vehicles within the actual driving environment have shown that combining multiple map-matching sources ensures more robust results than the use of a single environmental sensor.