RESUMEN
The future construction effort on the Moon and Mars is increasingly discussed by the scientific community. In authors' opinion quick, precise and remote measuring technique will be essential for successful development of lunar and Martian construction projects. One of such techniques is terrestrial laser scanning (TLS). The dataset consists of results obtained using two different, commercially available, laser scanners. The measurements were conducted on Earth using lunar and Martian soil simulants. As a reference (Earth soil simulant) a standardized sand used for cement tests was utilized. Scans were performed from different distances. The acquired point clouds can be used for thorough analysis of a laser beam dispersion and absorption. The comparison with other results is enabled. One should also keep in mind that some of the characteristics of Earth, the Moon and Mars which will influence TLS technique and measurements (e.g. local atmosphere or lack of it, temperatures, radiation, light, distances and colours).
RESUMEN
Floating autonomous vehicles are very often equipped with modern systems that collect information about the situation under the water surface, e.g., the depth or type of bottom and obstructions on the seafloor. One such system is the multibeam echosounder (MBES), which collects very large sets of bathymetric data. The development and analysis of such large sets are laborious and expensive. Reduction of the spatial data obtained from bathymetric and other systems collecting spatial data is currently widely used. In commercial programs used in the development of data from hydrographic systems, methods of interpolation to a specific mesh size are very frequently used. The authors of this article previously proposed original the true bathymetric data reduction method (TBDRed) and Optimum Dataset (OptD) reduction methods, which maintain the actual position and depth for each of the measured points, without their interpolation. The effectiveness of the proposed methods has already been presented in previous articles. This article proposes the fusion of original reduction methods, which is a new and innovative approach to the problem of bathymetric data reduction. The article contains a description of the methods used and the methodology of developing bathymetric data. The proposed fusion of reduction methods allows the generation of numerical models that can be a safe, reliable source of information, and a basis for design. Numerical models can also be used in comparative navigation, during the creation of electronic navigation maps and other hydrographic products.
RESUMEN
Cooperative positioning (CP) utilises information sharing among multiple nodes to enable positioning in Global Navigation Satellite System (GNSS)-denied environments. This paper reports the performance of a CP system for pedestrians using Ultra-Wide Band (UWB) technology inGNSS-denied environments. This data set was collected as part of a benchmarking measurementcampaign carried out at the Ohio State University in October 2017. Pedestrians were equippedwith a variety of sensors, including two different UWB systems, on a specially designed helmetserving as a mobile multi-sensor platform for CP. Different users were walking in stop-and-go modealong trajectories with predefined checkpoints and under various challenging environments. Inthe developed CP network, both Peer-to-Infrastructure (P2I) and Peer-to-Peer (P2P) measurementsare used for positioning of the pedestrians. It is realised that the proposed system can achievedecimetre-level accuracies (on average, around 20 cm) in the complete absence of GNSS signals,provided that the measurements from infrastructure nodes are available and the network geometryis good. In the absence of these good conditions, the results show that the average accuracydegrades to meter level. Further, it is experimentally demonstrated that inclusion of P2P cooperativerange observations further enhances the positioning accuracy and, in extreme cases when only oneinfrastructure measurement is available, P2P CP may reduce positioning errors by up to 95%. Thecomplete test setup, the methodology for development, and data collection are discussed in thispaper. In the next version of this system, additional observations such as theWi-Fi, camera, and othersignals of opportunity will be included.