Accurate surveying of vegetated areas presents significant challenges due to obstructions that obscure visibility and compromise the precision of measurements. This paper introduces a methodology employing the DJI Zenmuse L2 Light Detection and Ranging (LiDAR) sensor, which is mounted on a Matrice 350 RTK drone. The DJI Zenmuse L2 sensor excels at capturing detailed terrain data under heavy foliage, capable of collecting 1.2 million points per second and offering five returns, thus enhancing the sensor's ability to detect multiple surface responses from a single laser pulse. In a case study conducted near a creek heavily obscured by tree coverage, traditional aerial imaging techniques are found insufficient for capturing critical topographic features, such as the creek banks. Employing LiDAR, the study aims to map these obscured features effectively. The collected data is processed using DJI Terra software, which supports the accurate projection and analysis of the LiDAR data. To validate the accuracy of the data collected from the LiDAR sensor, traditional survey methods are deployed to ground truth the data and provide an accuracy assessment. Ground control points (GCPs) are established using a GNSS receiver to provide geodetic coordinates, which then assist in setting up a total station. This total station measures vertical and horizontal angles, as well as the slope distance from the instrument to positions underneath the tree coverage on the ground. These measurements serve as checkpoints to validate the accuracy of the LiDAR data, thus ensuring the reliability of the survey. This paper discusses the potential of integrating LiDAR data with traditional surveying data, which is expected to enhance the ability of surveyors to map environmental features efficiently and accurately in complex and vegetated terrains. Through detailed procedural descriptions and expected outcomes, the study aims to provide valuable insights into the strategic application of geospatial technologies to overcome common surveying challenges.

Autonomous driving offers benefits such as congestion mitigation, increased productivity through the reallocation of driving time, and decreased energy waste. However, achieving Level 4 and 5 autonomous driving remains a significant challenge for both academia and industry. Among the various modules of autonomous driving, High-Definition (HD) maps have become a crucial component due to their high precision in map elements, enabling accurate localization, scene interpretation, navigation, vehicle control and motion forecasting of trajectory of surrounding objects. Several map providers, including TomTom, HERE, Waymo, and NVIDIA, create HD maps for their specific purposes. However, most HD map datasets are not publicly available for individual researchers and companies to investigate the current trends in HD map generation. Furthermore, recent survey papers on HD map generation have tended to focus only on specific aspects, such as road topology or boundary extraction, rather than considering the overall end-to-end HD map generation process. Therefore, we begin with a brief definition, standards, and functionality of HD maps, followed by an exploration of different types of HD maps, including offline and online variants, highlighting their respective advantages and disadvantages. Finally, we will discuss the most recent end-to-end HD map generation architectures, along with various types of open-source HD map datasets and compare their performances.

The precise extraction of road boundaries is an essential task to obtain road infrastructure data that can support various applications, such as maintenance, autonomous driving, vehicle navigation, and the generation of high-definition maps (HD map). Despite promising outcomes in prior studies, challenges persist in road extraction, particularly in discerning diverse road types. The proposed methodology integrates state-of-the-art techniques like DBSCAN and RANSAC, aiming to establish a universally applicable approach for diverse mobile mapping systems. This effort represents a pioneering step in extracting road information from image-based point cloud data. To assess the efficacy of the proposed method, we conducted experiments using a large-scale dataset acquired by two mobile mapping systems on the Yıldız Technical University campus; one system was configured as a mobile LiDAR system (MLS), while the other was equipped with cameras to operate as a photogrammetry-based mobile mapping system (MMS). Using manually measured reference road boundary data, we evaluated the completeness, correctness, and quality parameters of the road extraction performance of our proposed method based on two datasets. The completeness rates were 93.2% and 84.5%, while the correctness rates were 98.6% and 93.6%, respectively. The overall quality of the road curb extraction was 93.9% and 84.5% for the two datasets. Our proposed algorithm is capable of accurately extracting straight or curved road boundaries and curbs from complex point cloud data that includes vehicles, pedestrians, and other obstacles in urban environment. Furthermore, our experiments demonstrate that the algorithm can be applied to point cloud data acquired from different systems, such as MLS and MMS, with varying spatial resolutions and accuracy levels.

In this article, we introduce a positioning system developed for two- and three-dimensional motion tracking. The system is based on a recursive Bayesian estimator with a dynamic naive Bayesian classifier map matching scheme. The states of the dynamic naive Bayesian classifier are created by using the map information and partitioning the region of interest into grids. The developed positioning system considers three types of measurements of the platform at each time instant: the heading measurement to determine the prior probability distribution; the single-anchor distance and altitude measurements to determine the observation likelihood. A recursive Bayesian estimator takes advantage of these measurements to obtain the posterior probability distribution. Ultimately, via the obtained posterior probability distribution, the most probable projection of these measurements onto the states of the dynamic naive Bayesian classifier is estimated as the current position of the platform. To avoid the potential ambiguities in the estimation process, the estimator exploits a design parameter that characterizes the platform’s maximum attainable speed. Simulations and real-time application results are given to illustrate the effectiveness of the developed system for positioning applications in two- and three-dimensional indoor and outdoor environments with constraints, such as corridors, roads, or flight paths.

Given the indoor unavailability of Global Navigation Satellite Systems (GNSS), indoor positioning has to rely on other sensors, such as inertial sensors, radio signals, vision (cameras) and LiDAR (Light Detection and Ranging). Among the radio signals, thanks to its quite precise measurements, only moderately impacted by the presence of obstacles in the environment, UWB (Ultra-Wide Band) technology has been successfully used in several positioning systems, enabling localization at submeter accuracy. Due to the recent introduction of UWB transceivers in the new generation of smartphones, the use of this technology in positioning applications in real scenarios is expected to grow in the future. Reliable UWB-based positioning typically depends on the availability of a dense UWB static infrastructure, where ranges between a rover transceiver and the nodes of such static network are used to trilaterate the moving device position. Since the development and maintenance of such an infrastructure may be expensive, exploiting ranging measurements between different moving platforms can be a viable option to reduce the need for dense static infrastructure and can increase the overall performance of the positioning system. This study investigates the effectiveness of UWB-based indoor collaborative navigation. In particular, the obtained results show that the collaborative approach may significantly improve the solution availability and could support range outlier rejection.

The aim of this work is to develop an approach that enables Unmanned Aerial System (UAS) to efficiently learn to navigate in large-scale urban environments and transfer their acquired expertise to novel environments. To achieve this, we propose a meta-curriculum training scheme. First, meta-training allows the agent to learn a master policy to generalize across tasks. The resulting model is then fine-tuned on the downstream tasks. We organize the training curriculum in a hierarchical manner such that the agent is guided from coarse to fine towards the target task. In addition, we introduce Incremental Self-Adaptive Reinforcement learning (ISAR), an algorithm that combines the ideas of incremental learning and meta-reinforcement learning (MRL). In contrast to traditional reinforcement learning (RL), which focuses on acquiring a policy for a specific task, MRL aims to learn a policy with fast transfer ability to novel tasks. However, the MRL training process is time consuming, whereas our proposed ISAR algorithm achieves faster convergence than the conventional MRL algorithm. We evaluate the proposed methodologies in simulated environments and demonstrate that using this training philosophy in conjunction with the ISAR algorithm significantly improves the convergence speed for navigation in large-scale cities and the adaptation proficiency in novel environments. The project page is publicly available at https://superhan2611.github.io/isar_nav/.

Aerial mapping using Unmanned Aerial Systems (UAS), such as the DJI Mavic 3 Enterprise, has revolutionized photogrammetry, enabling efficient data capture for small-scale projects. The typical nadir perspective of UAS mapping, however, imposes limitations on capturing critical details of features due to its predominantly vertical viewpoint. Overcoming this challenge often requires manual, low-altitude flights by experienced UAS pilots to achieve high-angle oblique perspectives, unless gimbled camera mount is used. This study explores the integration of high oblique angle perspectives using the iPhone 15 Pro, which boasts advanced camera capabilities and an integrated LiDAR sensor, to complement UAS imagery. The iPhone 15 Pro's camera sensors provide a Ground Sampled Distance (GSD) comparable to UAS cameras, while its LiDAR sensor, with about five meters of range, enhances mapping capabilities by delivering accurate depth measurements in close range. By utilizing various georeferencing options for the imagery and LiDAR data from the iPhone 15 Pro with UAS nadir imagery, we can achieve a more comprehensive object space reconstruction, significantly improving the accuracy of geospatial mapping. Both the Mavic 3 Enterprise and the iPhone 15 Pro, though operating independently on their respective platforms, support Real-Time Kinematic (RTK) corrections, facilitating precise positioning for the entire system trajectory. Strategic placement and utilization of Ground Control Points (GCPs) aid in the georeferencing of the complete dataset, enhancing its overall accuracy. To validate the accuracy of the acquired data, checkpoints are established on-site. The positions derived from the integrated UAS and iPhone 15 Pro data are compared against these checkpoints to quantify the accuracy and reliability of the data. Additionally, Post-Processed Kinematic (PPK) techniques are employed to validate the trajectories of all data collection systems, ensuring the reliability of the acquired data, especially in instances where RTK corrections may be lacking. In summary, this research showcases comprehensive, multi-dimensional geospatial datasets by conducting validation studies that assess the accuracy and reliability of georeferenced datasets against known ground truth checkpoints. Such validation studies are crucial for identifying gaps in current methodologies and suggesting areas for improvement, thereby aiming to enhance the quality and accuracy of geospatial mapping applications. Through the integration of UAS and smartphone mapping, complemented by rigorous validation efforts, we aspire to achieve improved geospatial mapping accuracy.

This research introduces a novel approach to improve vision-based positioning in the absence of GNSS signals. Specifically, we address the challenge posed by obstacles that alter image information or features, making retrieving the query image from the database difficult. While the Bag of Visual Words (BoVW) is a widely used image retrieval technique, it has a limitation in representing each image with a single histogram vector or vocabulary of visual words, i.e., the emergence of obstacles can introduce new features to the query image, resulting in different visual words. Our study overcomes this limitation by clustering the features of each image using the k-means method and generating a graph for each class. Each node or key point in the graph obtains additional information from its direct neighbors using functions employed in graph neural networks, functioning as a feedforward network with constant parameters. This process generates new embedding nodes, and eventually, global pooling is applied to produce one vector for each graph, representing each image with graph vectors based on objects or feature classes. As a result, each image is represented with graph vectors based on objects or feature classes. In the presence of obstacles covering one or more graphs, there is sufficient information from the query image to retrieve the most relevant image from the database. Our approach was applied to indoor positioning applications, with the database collected in Bolz Hall at The Ohio State University. Traditional BoVW techniques struggle to properly retrieve most query images from the database due to obstacles like humans or recently deployed objects that alter image features. In contrast, our approach has shown progress in image retrieval by representing each image with multiple graph vectors, depending on the number of objects in the image. This helps prevent or mitigate changes in image features caused by obstacles covering or adding features to the image, as demonstrated in the results.

The 2023 Kahramanmaraş earthquake sequence produced extensive liquefaction-induced ground deformations and ongoing flooding along the shoreline of the Mediterranean port city of İskenderun, Türkiye. This study compiles field observations and analyses from cross-disciplinary perspectives to investigate whether earthquake-induced liquefaction was a significant factor for increasing the flood hazard in İskenderun. Geotechnical reconnaissance observations following the earthquakes included seaward lateral spreading, settlement beneath buildings, and failures of coastal infrastructure. Three presented lateral spreading case histories indicate consistent ground deformation patterns with areas of reclaimed land. Persistent scatterer interferometry (PSI) measurements from synthetic aperture radar (SAR) imagery identify a noticeably greater rate of pre- and post-earthquake subsidence within the İskenderun coastal and urban areas relative to the surrounding regions. The PSI measurements also indicate subsidence rates accelerated following the earthquakes and were typically highest near the observed liquefaction manifestations. These evaluations suggest that while the liquefaction of coastal reclaimed fill caused significant ground deformations in the shoreline area, ongoing subsidence of İskenderun and other factors likely also exacerbated the flood hazard. Insights from this work suggest the importance of evaluating multi-hazard liquefaction and flood consequences for enhancing the resilience of coastal cities.

Abstract. Utilizing ground control points (GCPs) to georeference photogrammetry-based point cloud data is a common practice in unmanned aerial system (UAS) mapping. Direct georeferencing or integrated sensor orientation (ISO) can be used to obtain georeferenced point clouds from UAS without relying heavily on GCPs. However, the accuracy of the point cloud may be impacted by the accuracy of the trajectory solution obtained by GNSS. To improve point cloud accuracy, post-processing kinematic (PPK) solutions can be applied to the UAS trajectory, which may provide higher accuracy than low-accuracy trajectory solutions and minimize the reliance on GCPs. This study compares the accuracy and precision of two different point clouds generated using different methods. One point cloud was generated using traditional photogrammetric methods with low accuracy Global Navigation Satellite System (GNSS) observations from the UAS and GCPs that have an average accuracy of one to two centimeters, while the other was generated using PPK trajectory solution for the UAS’s trajectory with two software: open-source Emlid Studio and the widely used Inertial Explorer. The use of PPK techniques in UAS mapping may have several potential benefits over traditional methods. By correcting the errors in the UAS's trajectory, a user may only need to depend on fewer ground control points, which can reduce the time and cost associated with fieldwork. This is particularly useful in areas that are difficult to access or have limited ground control point options, such as in urban or forested areas. To evaluate performance, a GNSS receiver is used to obtain measurements on checkpoints, which are used to assess the accuracies of the point clouds. In our experiments, the accuracy of the point clouds generated using PPK trajectory solution with high accuracy GCPs was found to be higher than those generated with low accuracy GNSS observations while aided with high accuracy ground control points. While the use of PPK with GCPs is generally expected to provide more accurate and reliable data than low-accuracy GNSS observations even after adjusting with GCPs, the number and distribution of GCPs can still significantly impact overall accuracy. Therefore, careful consideration of the number of GCPs and their placement is essential to achieve the desired level of efficiency and effectiveness in UAS mapping.

Collaborative navigation entails a concept of a group of platforms navigating collectively and supporting each other's positioning solution to obtain higher accuracy and availability for all platforms. It is regarded as a promising solution to meet the demands on accurate positioning and real-time situation awareness in future Intelligent Transportation Systems. Integrity is a measure of trust that can be placed on the information and timely warnings from a navigation system. Integrity monitoring is mostly solved as a hypothesis testing problem in measurement domain and solution domain. This paper explores and investigates the integrity monitoring of collaborative navigation. Two methods are adopted here. The first method is based on the least-squares adjustment of the internodal range measurements to detect any anomalies in GNSS solutions. There is a rank deficiency problem associated with it. Another method is based on Akaike Information Criterion (AIC) that was originally developed for model selection. Tests on partially simulated data sets demonstrate that adjustment methods are effective in detecting GNSS anomalies and better than the innovation-based method for cases with clean range measurements. The AIC based method, however, is able to detect and identify multiple simultaneous GNSS and internodal range outliers.

Despite GNSS (Global Navigation Satellite System) enables positioning, navigation and timing (PNT) almost everywhere, the development of applications like self-driving vehicles and indoor navigation requires extending accurate positioning to scenarios where GNSS either is not reliable or does garantee a sufficiently precise solution. Integrating inforamtion provided by different sensors is commonly accepted to be a quite viable way for such extension. In particular this work is part of a project aiming at investigating the positioning performance that can be obtained by integrating vision with radio-based systems and inertial sensors, which are commonly installed on many smart devices, such as smartphones. Furthermore, this work considers positioning in a collaborative scenario, where different interconnected platforms, i.e. unmanned aerial vehicles and pedestrians provided with smartphones, are moving on the same area. The results obtained in the considered tests show a good potential (submetric 2D positioning error) for what concerns the implemented strategies, where the integration of different technologies can ensure decent performance in a wider range of working cases.

Abstract. The development of autonomous vehicles, both terrestrial and aerial ones (unmanned aerial system (UAS)), is causing the need of properly formulating appropriate solutions for ensuring a safe interaction between them, human beings and the infrastructures and environment in their operating area. To such aim, the knowledge of the positions of different platforms moving in the considered area is fundamental. GNSS (Global Navigation Satellite System) is by far the most used positioning technique in order to determine positions all over the world. Nevertheless, there are several conditions in which its use is unfortunately impossible or unreliable. Hence, different techniques, based on the use of sensors either mounted on the moving platforms or on an ad-hoc infrastructure, shall be used in order to determine the absolute and relative positions of the involved platforms. To this aim, this work proposes the use of vision, in particular from UAS imagery, static LiDAR (Light Detection and Ranging) and UWB (Ultra Wide-Band) transceivers, with initial encouraging results.

Nowadays, Positioning, Navigation and Timing (PNT) systems play a key role in many applications, ranging from vehicle to personal navigation to Location Based Services (LBS). In fact, the availability of GNSS-based PNT systems enabled the development of new applications and solutions in many fields. While outdoors precise solutions can be obtained in a wide range of environmental conditions, there are still a number of situations, such as indoors, in tunnels and urban canyons, where it is hard to achieve a good navigation solution due to the unreliability or unavailability of GNSS (Global Navigation satellite System). Therefore, there is a strong motivation to search for alternative methods in order to provide reliable positioning in challenging scenarios. Sensor integration, combining information provided by multiple sensors is commonly accepted as the primary approach to obtain navigation solution in GNSS-denied environment. The increasing deployment of connected devices, including assisted and autonomous vehicles, however, offers the possibility of implementing collaborative strategies within the network of interconnected platforms. This work is part of an ongoing project that aims at investigating the development of collaborative positioning and navigation of ground and aerial platforms. In the implemented scalable distributed collaborative positioning approach, each platform runs an Extended Kalman filter (EKF), where the state vector of each of such EKFs contains the corresponding platform position, velocity and acceleration variables. In addition, range observations and communication from Ultra-Wide Band (UWB) and LiDAR (Light Detection and Ranging) are considered in each EKF, involving only platforms in the close neighborhood of the considered agent. This paper presents basic characteristics of a dataset collected to investigate the performance of joint collaborative navigation of air and ground platforms and the obtained initial results.

Most computer vision and photogrammetry applications rely on accurately estimating the camera pose, such as visual navigation, motion tracking, stereo photogrammetry, and structure from motion. The Essential matrix is a well-known model in computer vision that provides information about the relative orientation between two images, including the rotation and translation, for calibrated cameras with a known camera matrix. To estimate the Essential matrix, the camera calibration matrices, which include focal length and principal point location must be known, and the estimation process typically requires at least five matching points and the use of robust algorithms, such as RANSAC to fit a model to the data as a robust estimator. From the usually large number of matched points, choosing five points, the Essential matrix can be determined based on a simple solution, which could be good or bad. Obtaining a globally optimal and accurate camera pose estimation, however, requires additional steps, such as using evolutionary algorithms (EA) or swarm algorithms (SA), to prevent getting trapped in local optima by searching for solutions within a potentially huge solution space.This paper aims to introduce an improved method for estimating the Essential matrix using swarm particle algorithms that are known to efficiently solve complex problems. Various optimization techniques, including EAs and SAs, such as Particle Swarm Optimization (PSO), Gray Wolf Optimization (GWO), Improved Gray Wolf Optimization (IGWO), Genetic Algorithm (GA), Salp Swarm Algorithm (SSA) and Whale Optimization Algorithm (WOA), are explored to obtain the global minimum of the reprojection error for the five-point Essential matrix estimation based on using symmetric geometric error cost function. The experimental results on a dataset with known camera orientation demonstrate that the IGWO method has achieved the best score compared to other techniques and significantly speeds up the camera pose estimation for larger number of point pairs in contrast to traditional methods that use the collinearity equations in an iterative adjustment.

In this study, we investigate the feasibility of using an iPhone 14 Pro's camera and LiDAR sensors to collect high-accuracy spatial data on a mobile e-scooter. Given the widespread availability of e-scooters in urban areas, they present an ideal platform for creating a compact mobile mapping system. The iPhone is securely mounted on the e-scooter and paired with a viDoc RTK Rover, which offers real-time kinematic (RTK) positioning accuracy in open sky areas. As the e-scooter traverses the area of interest, data is collected using the LiDAR sensor, while images are captured using the camera. The collected data is then processed using Pix4Dmatic software, enabling the generation of a fused point cloud and a detailed digital model of the surveyed area. In situations where the Global Navigation Satellite System (GNSS) signal is compromised or unavailable, such as indoor environments or urban canyons, alternative methods like Simultaneous Localization and Mapping (SLAM) can be employed. Additionally, Total Stations can be utilized to track the entire system's movement in GNSS-denied environments and provide accurate georeferencing for the acquired data. Control and check points throughout the area of interest are established using the Total Station as well. This approach offers a flexible and cost-effective means of collecting high-accuracy spatial data in small areas across a variety of environments, leveraging the readily available e-scooters for public use. The results of various experiments conducted using an iPhone 14 Pro and viDoc RTK on an e-scooter are thoroughly analyzed and reported in this paper.

This paper proposes a two-dimensonal positioning method based on a hidden Markov model map-matching scheme. The states of the hidden Markov model are generated by dividing the area of interest into a grid. At each time instant, the method considers two types of measurements: the platform’s heading and the two-dimensional distance between the platform and the single-anchor. A recursive Bayesian estimator exploits these measurements to estimate the platform’s position. The platform’s heading measurement is used to calculate the prior probability distribution. Following this, observation likelihood is computed by considering the two-dimensional distance measurement as the observation of the hidden Markov model. Finally, the most probable projection of these measurements on the states of the hidden Markov model is estimated as the platform’s position. The proposed method can be efficiently used, especially in constrained indoor and outdoor environments. Moreover, the method provides a two-dimensional positioning solution with an increased robustness thanks to the bounded error on the distance measurements. Simulation studies are provided to demonstrate the effectiveness of the proposed method.

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.

Digital documentation of historical sites has always required the use of expensive professional grade sensors capable of collecting large amounts of data to reconstruct cultural sites. These types of projects generally require large budgets and a large team of specialists to successfully generate a digital model. However, with smart devices having sensors capable of mapping on the go, the potential for mapping such historical sites may be more accessible. This study aims to conduct a comprehensive comparison between the iPhone 13 Pro and the Unmanned Aerial Systems (UAS) photogrammetric model of the Great Pyramid of Giza, otherwise known as the Khufu pyramid, located in Giza, Egypt. The purpose of this study is to evaluate the potential of the iPhone 13 Pro's Camera and LiDAR sensor capabilities as a valuable tool for documenting and preserving cultural heritage sites. To accomplish this, data was captured from multiple positions around the pyramid using the Pix4Dcatch app on the iPhone 13 Pro, and the data was processed using Pix4Dmatic to generate a 3D point cloud of the pyramid. This point cloud data is then compared to the reference data obtained through the UAS mapping which generated a 3D photogrammetric model. The comparison aims to identify the strengths and weaknesses of using the iPhone 13 Pro for this type of scanning and to assess the accuracy and precision of the generated data.

The availability of Global Positioning System (GPS), or more in general of Global Navigation Satellite Systems (GNSS), and the development of smart mobile devices, able to exploit the geospatial information provided by GPS/GNSS and integrate their use within many applications, have had a dramatic impact on the everyday life of most of the World population. While GNSS allows for real-time positioning in a wide range of scenarios, there are many challenging environments, such as tunnels and urban canyons, where GNSS-based solutions are inaccurate, unreliable, or even unavailable. The enormous interest in applications requiring ubiquitous positioning (e.g., self-driving vehicles) has been motivating the development of alternative positioning systems to support or substitute GNSS when operating in challenging scenarios. Alternative positioning systems to GNSS are usually developed by employing several sensors, such as radio-based, vision, LiDAR (Light Detection and Ranging), and RADAR (Radio Detection and Ranging). Furthermore, a collaborative approach can also be developed to increase the robustness of the navigation solution of inter-connected vehicles. To support research in this area, we are presenting the CONTEST (Collaborative pOsitioning and NavigaTion bEtween ground and uaS plaTforms) dataset, aiming at providing multiple data streams to test collaborative positioning approaches, involving both terrestrial and aerial platforms, based on the use of several sensors, such as Ultra-Wide Band (UWB) transceivers, cameras, LiDARs, GNSS. Data are described and some initial results presented.