Abstract. Mapping platforms jointly operating in a formation are increasingly used to improve the efficiency of geospatial data acquisition recently. For example, UAS swarm mapping is gaining market share or robot platform are used for indoor mapping. These platforms are typically equipped with imaging and navigation sensors as well as have various communication capabilities. Until now, the platforms are individually navigated and georeferenced. Since the platforms are typically sharing a small area, and thus, they are within their sensing range; for example, they can see and thus track each other in optical or lidar imagery. Furthermore, new communication technologies have started to provide ranging information between communication points. Using the ranges between platforms makes it feasible to create a local geodetic network defined by the platforms. The geometric strength of the network then can be exploited to support platform georeferencing. In this study, the network formation based on ranges is investigated. Initial experiences are reported on the impact of the size of the network, the spatial distribution of the network nodes and the number of available ranges.

Abstract. The Smart City concept is taking momentum recently as big metropolises as well as mid-size cities are intensifying their efforts to improve the life of people living in dense urban environment. Local governments are eager to have up-to-date information of every aspect of city life, including environmental data, such as air and water quality parameters; mobility data, such as traffic flow, including vehicles, transit passengers; crowd control, such as public events, mobility in hospitals; life quality data, such as social status, education level, health records; etc. Monitoring all these very different data streams in space and time is a formidable challenge. While on the data acquisition side, tremendous progress has been achieved, as sensors have been deployed in increasingly large numbers on both mobile and static platforms, there is a lack of creating accurate geotags, as the quality of georeferencing varies over a large scale. It is important to note that the data acquisition is becoming largely customer-based, as smart devices are efficient sensor systems and with advancing communication technologies, crowdsourcing is quickly becoming the dominant data source on mobile platforms. In this paper, we investigate the potential to exploit the ranging capabilities of imaging and communication sensors and use the strength of the spatial network formed by the sensors to improve the georeferencing of a group of platforms operating in a close environment, such as UAS swarm or a platoon of autonomous vehicles. Transportation in cities and in general mobility are of great interest to Smart Cities, they represent one of the most significant components of the activities, so having an optimized transportation system is essential to reduce carbon footprint, decrease commute time, and just improve the quality of life. To assess the performance of collaborative navigation based accurate georeferencing, data was acquired at a simulated intersection area at The Ohio State University, where multiple vehicles, pedestrians and cyclists were moving around. In addition, drones were flying above the area. Here we report about our initial results.

Abstract. The use of BIM (Building Information Modeling), a component of the Digital Twin concept, is on the rise, and the need for indoor data is rapidly growing. BIM information is not only used for management purposes, but it is essential to support navigation indoors. Observing building interiors by optical sensors, such as cameras and laser scanners, has challenges as the image scale changes over a broad range in rooms and floors, and then complete coverage is required, requiring images taken from several locations with various camera orientations. Using 360° imaging sensors partially addresses the need for efficient wide FOV observations. In this study, we investigate the feasibility of using a 6-sensor omnidirectional/fisheye camera system and report about its performance.