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Research Roundup: Navigating urban canyons

Tall buildings block GNSS signals, making satellite navigation in urban canyons very challenging. (Photo: RoschetzkyIstockPhoto/iStock/Getty Images Plus/Getty Images)

Tall buildings block GNSS signals, making satellite navigation in urban canyons very challenging. (Photo: RoschetzkyIstockPhoto/iStock/Getty Images Plus/Getty Images)

GPS positioning for navigation and mapping is challenging in urban environments, where GPS signals often are blocked by tall buildings. The following three papers — to be presented at the Institute of Navigation (ION) GNSS+ conference Sept. 19–23, 2022 — explore ways to solve that problem. The full papers will be available at following the conference.


In this work, the authors use an urban environment model incorporating visibility predictions and remote-sensing techniques, which they tested in a sensor-equipped vehicle in Denver. They use an interacting multiple model (IMM) filter that uses extended Kalman filters to build and verify a map of the signal environment in an urban-canyon setting. The techniques will give ground-vehicle operations the ability to plan for blocked and delayed signals for global path planning.

Zeller, Emma; Strandjord, Kirsten, University of Minnesota; and Wang, Pai, Shanghai Jiao Tong University; “Algorithms for Mapping the Urban Signal Environment for Navigation of Ground Vehicle Operations.”


GNSS real-time kinematic (GNSS-RTK) positioning is a key technology for surveying and mapping applications. To extend the capability of GNSS in difficult environments, a tight coupling between GNSS-RTK and an inertial navigation system (INS) can greatly improve the results. If the time spent in a GNSS outage is too long or if the kinematic of the survey is too weak, the GNSS/INS solution can be compromised with high navigation errors, ultimately making it impossible to align the heading angle at initialization.

This paper presents an innovative solution to overcome GNSS/INS limitations, minimizing system complexity by using a tightly coupled GNSS/INS solution with a monocular visual inertial SLAM system. This solution is capable of initialization in a few seconds and is very reliable in the long term. This vision/INS/GNSS coupling increases the overall RTK fix rate and broadens the availability of high-precision navigation solutions under challenging conditions.

Bénet, Pierre; Saussay, Brice; Saidani, Mourad; and Guinamard, Alexis; SBG Systems; “Tightly Coupled Inertial Visual GNSS Solution: Application to LIDAR Mapping in Harsh and Denied GNSS Conditions.”


To solve the urban-navigation challenge, the authors propose using a 3D building model to assist GNSS positioning. This type of algorithm is named the 3D building model aided GNSS (3DMA GNSS). It can predict measurement errors and the visibility of the satellites, as line-of-sight or non-line-of-sight. The solution is then derived from the likelihood of the observed and predicted measurements over candidate locations.

The authors propose an innovative method for evaluating the reliability of building models based on the awareness of sky visibility in a specific geographic context. Sky visibility estimation is improved with use of a support vector machine regression and considering low-Earth-orbit (LEO) constellations. The real-time sky visibility could present the update of the surrounding buildings, whereas the predicted sky visibility based on the existing building models remains unchanged. Making use of this inconsistency, the authors could identify areas with the updated building. Additionally, the impacts of the building update monitoring on the 3DMA GNSS are evaluated in an urban canyon.

Xu, Hao-Sheng and Hsu, Li-Ta; Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University; “Urban Buildings Update Monitoring Based on Sky Visibility Estimation using GNSS and LEO.”

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