Mobile mapping relies on satellite‐based positioning, which suffers from line‐of‐sight and multipath issues. As an alternative, this paper presents a fully automatic approach for the co‐registration of mobile mapping and oblique aerial images to introduce highly accurate and reliable ground control for mobile mapping data adjustment. An oblique view of a scene introduces similarities as well as challenges regarding co‐registration with mobile mapping images, which is supported by mutual planes in both datasets. Façade planes from a sparse point cloud are used as projection surfaces for the mobile mapping and aerial datasets, overcoming large perspective differences between them to simplify the registration. The performance of the procedure indicates an inlier rate of around 80%.

Abstract. In recent years, the proliferation and further development of unmanned aerial vehicles (UAVs) led to a great number of key technologies, advances and opportunities especially in the realm of time-critical applications. UAVs as a platform provide a unique combination of flexibility, affordability and sensor technology which enables the design of cost-effective and intriguing services particularly for disaster response. This contribution presents a concept for UAV-based near real-time mapping system for disaster relief to provide decision-making support for first responders particularly for possible disaster scenarios in Austria. We outline our system concept and its respective architecture, discuss requirements from a stakeholder perspective as well as legal regulations and initiatives at an EU level. In the methodology section of this paper, the preliminary data processing pipeline with respect to the near real-time orthomosaic generation and the semantic segmentation network are presented. Lastly, first experimental results of the pipeline are shown, and further advances are discussed.

Abstract. UAVs have become an indispensable tool for a variety of mapping applications. Not only in the area of surveying, infrastructure planning and environmental monitoring tasks but also in time-critical applications, such as emergency and disaster response. Although UAVs enable rapid data acquisition per se, data processing usually relies on offline workflows. This contribution presents an accurate real-time data processing solution for UAV mapping applications as well as an extensive experimental and comparative study to the commercial offline solution Pix4D on the absolute accuracy of orthomosaics and digital surface models. We show that our procedure achieves an absolute horizontal and vertical accuracy of about 1 m without the use of ground control. The code will be made publicly available.

<p><strong>Abstract.</strong> Mobile mapping enables highly accurate as well as high-resolution image data capture at low cost and high speed. As a terrestrial acquisition technique predominately employed in urban, and thus built-up areas, non-line-of-sight and multipath effects challenge its absolute positioning capabilities provided by GNSS. In conjunction with IMU drift, the platform’s trajectory has an unknown accuracy, which influences the quality of the data product. By employing a highly accurate co-registration technique for identifying tie correspondences between mobile mapping images and aerial nadir as well as aerial oblique images, reliable ground control can be introduced into an adjustment solution. We exemplify the performance of our registration results by showcasing adjusted mobile mapping trajectories in four different test areas, each with about 100 consecutive recording locations (approx. 500&amp;thinsp;m length) in the city centre of Rotterdam, The Netherlands. The mobile mapping data has been adjusted in different configurations, i.e. with nadir or oblique aerial correspondences only and if possible in conjunction. To compare the horizontal as well as the vertical accuracy before and after the respective adjustments, more than 30 ground control points were surveyed for these experiments. In general, the aim of our technique is not only to correct mobile mapping trajectories in an automated fashion but also to verify their accuracy without the need to acquire ground control points. In most of our test cases, the overall accuracy of the mobile mapping image positions in the trajectory could be improved. Depending on the test area, an RMSE in 3D between 15 and 21&amp;thinsp;cm and an RMSE in 2D between 11 and 18&amp;thinsp;cm is achievable.</p>

Whilst mapping with UAVs has become an established tool for geodata acquisition in many domains, certain time-critical applications, such as crisis and disaster response, demand fast geodata processing pipelines rather than photogrammetric post-processing approaches. Based on our 3D-capable real-time mapping pipeline, this contribution presents not only an array of optimisations of the original implementation but also an extension towards understanding the image content with respect to land cover and object detection using machine learning. This paper (1) describes the pipeline in its entirety, (2) compares the performance of the semantic labelling and object detection models quantitatively and (3) showcases real-world experiments with qualitative evaluations.

Abstract. Exploration is a fundamental problem in robotics that requires robots to navigate through unknown environments to autonomously gather information about their surroundings while executing collision-free paths. In this paper, we propose a method for producing smooth paths during the exploration process in indoor environments using UAVs to improve battery efficiency and enhance the quality of pose estimation. The developed framework is built by merging two approaches that represent the state of the art in the field of autonomous exploration with UAVs. The overall exploration logic is given by GLocal, a paper that introduces an hybrid, i.e. both sampling-based and frontier-based, framework that is able to cope with the issue of odometry drift when exploring indoor environments due to the absence of absolute localization, e.g. through GNSS. The second paper is FUEL, which introduces a frontier-based exploration methodology which computes the drone’s path as an optimized non-uniform B-Spline. The framework described in this paper borrows the optimized B-Spline trajectory generation from FUEL and implements it in GLocal. The presented system is evaluated in two different simulated environments, which show the pros and the cons of such method.