Global Navigation Satellite Systems (GNSS) deliver absolute position and velocity, as well as time information (P, V, T). However, in urban areas, the GNSS navigation performance is restricted due to signal obstructions and multipath. This is especially true for applications dealing with highly automatic or even autonomous driving. Subsequently, multi-sensor platforms including laser scanners and cameras, as well as map data are used to enhance the navigation performance, namely in accuracy, integrity, continuity and availability. Although well-established procedures for integrity monitoring exist for aircraft navigation, for sensors and fusion algorithms used in automotive navigation, these concepts are still lacking. The research training group i.c.sens, integrity and collaboration in dynamic sensor networks, aims to fill this gap and to contribute to relevant topics. This includes the definition of alternative integrity concepts for space and time based on set theory and interval mathematics, establishing new types of maps that report on the trustworthiness of the represented information, as well as taking advantage of collaboration by improved filters incorporating person and object tracking. In this paper, we describe our approach and summarize the preliminary results.

We investigate whether conditional generative adversarial networks (C-GANs) are suitable for point cloud rendering. For this purpose, we created a dataset containing approximately 150,000 renderings of point cloud–image pairs. The dataset was recorded using our mobile mapping system, with capture dates that spread across 1 year. Our model learns how to predict realistically looking images from just point cloud data. We show that we can use this approach to colourize point clouds without the usage of any camera images. Additionally, we show that by parameterizing the recording date, we are even able to predict realistically looking views for different seasons, from identical input point clouds.

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