Radio propagation models for differential GNSS based on dense point clouds

Abstract

Accurate geolocation of mobile equipment operating in outdoor environments is an increasingly important question in robotics and automation. Modern geolocation systems, however, rely on the crucial ability for a mobile device to receive specific radio signals at all times. As such geolocation systems are increasingly deployed in harsh or difficult environments, for example, in the presence of tall buildings or dense forest, it becomes critical to predict how the environment will impact the propagation of these radio signals. To this effect, we present a new signal propagation model that can determine what areas would be favorable for global navigation satellite system (GNSS) positioning, based on a prior three-dimensional (3D) point cloud map of the environment. Our model can predict both the number of usable satellites for a GNSS receiver and the strength of the reference radio signal used in the differential GNSS scenario. Contrary to others, it takes into account both signal occlusion and absorption mechanisms, given the geometry and density of the point cloud map. We designed two rugged mobile data-collecting platforms, both to generate the 3D maps of the environment, as well as to gather various ground truth for GNSS satellite and local radio signals. Environments used for our field deployments included a boreal forest, a subarctic forest and diverse industrial areas. Experimental results indicate that our model performs well in both structured and unstructured environments, with median errors of 1.10 for the predicted number of satellites and 1.85 dBm for the strength of the differential GNSS correction signals.

PropEM-L: Radio Propagation Environment Modeling and Learning for Communication-Aware Multi-Robot Exploration

“…Previous work in the ecology community has considered extracting relevant information from 3D point cloud data. The authors of [6] and [16] use point clouds to determine the location and thickness of trees in a dense forest, which could theoretically feed into the model presented in [12]. Recently, the authors of [10] extend this idea to use learningbased methods on 3D point cloud data to predict the affect of tree canopies on signals propagating between static wireless communication towers.…”

Accurate outdoor ground truth based on total stations

“…To ground the performance of the total station referencing system to a commonly-known alternative, we compared it to the RTK solution. In our previous work [8], we have studied the impact of vegetation and obstacles both on the propagation of the satellite signal and the base station correction signal. The satellite localization performed well in open areas, but a decrease in accuracy was observed when portions of the sky view were obstructed by vegetation.…”

“…When it comes to gathering datasets in forests, such as the one depicted in Figure 1, accuracy of Real Time Kinematics (RTK) positioning suffers from signal attenuation by vegetation [8]. Consequently, the need for accurate and precise pose reference immune to foliage interference and signal reflection has motivated the development of a system based on multiple total stations appropriate for such task.…”

A Cooperative Positioning Method of Connected and Automated Vehicles with Direction-of-Arrival and Relative Distance Fusion

“…Various techniques have been proposed to improve vehicular positioning accuracy further to meet multivehicle cooperative control requirements [11][12][13][14][15]. Differential GNSS is proposed in [16] to achieve higher positioning accuracy by eliminating the common biases through a network of fixed reference stations. e real-time kinematic (RTK) technique is employed based on carrier phase measurements in [17] to realize centimeter-level accuracy.…”