3D map creation using crowdsourced GNSS data
This addresses the high cost of 3D map creation for applications like drone navigation and urban planning, though it is incremental in using existing GNSS signals.
The paper tackled the problem of creating 3D maps by proposing a novel approach that uses crowdsourced GNSS data to generate 2.5D maps for free, achieving height estimation accuracy below 5 meters as recommended by the CityGML standard.
3D maps are increasingly useful for many applications such as drone navigation, emergency services, and urban planning. However, creating 3D maps and keeping them up-to-date using existing technologies, such as laser scanners, is expensive. This paper proposes and implements a novel approach to generate 2.5D (otherwise known as 3D level-of-detail (LOD) 1) maps for free using Global Navigation Satellite Systems (GNSS) signals, which are globally available and are blocked only by obstacles between the satellites and the receivers. This enables us to find the patterns of GNSS signal availability and create 3D maps. The paper applies algorithms to GNSS signal strength patterns based on a boot-strapped technique that iteratively trains the signal classifiers while generating the map. Results of the proposed technique demonstrate the ability to create 3D maps using automatically processed GNSS data. The results show that the third dimension, i.e. height of the buildings, can be estimated with below 5 metre accuracy, which is the benchmark recommended by the CityGML standard.