Accurate and reliable positioning is an important prerequisite for numerous vehicular applications. Localization techniques based on satellite navigation systems are nowadays standard and deployed in most commercial vehicles. When such a standalone positioning is used in challenging environmentslike dense urban areas, the localization performance often dramatically degrades due to blocked and reflected satellites signals. In this paper, a general and lightweight probabilis tic positioning algorithm with integrated multi path detection through 3D environmental building models is presented. It will be shown that the proposed system outperforms-in terms of accuracy and integrity-existing methods without introducing additional hardware sensors. Furthermore, a benefit analysis of the suggested 3D model for tightly and loosely coupled GPSIINS sensor integration schemas is provided. Finally, the algorithm will be evaluated with real-world data collected during an urban measurement campaign.
Reliable knowledge of the ego position for vehicles is a crucial requirement for many automotive applications. In order to solve this problem for satellite-based localization in dense urban areas, multipath situations need to be handled carefully. This paper proposes a lightweight multipath detection algorithm which is based on dynamically built 3D environmental maps. The algorithm is evaluated with simulated and real-world data. Furthermore, it is applied to a combined GPS and GLONASS system in combination with a loosely coupled integration of odometry measurements from the vehicle.
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