Cooperative networks of low-cost unmanned aerial vehicles (UAVs) are attracting researchers because of their potential to enhance UAV performance. Cooperative networks can be used in many applications, including assisted guidance and navigation, surveillance, search and rescue, disaster management, defense, mapping, precision agriculture, and mineral exploration. Such cooperative networks of UAVs can act as ad hoc networks and share information among different network nodes. Such information sharing makes these nodes more robust and efficient for the intended purpose. The location of UAVs is traditionally determined using a global navigation satellite system (GNSS), which limits the use of UAVs in regions that lack GNSS. However, the location of UAVs can be determined even in environments without GNSS through a cooperative network if a few of the nodes have access to GNSS. This is achieved by sharing the information among the nodes of the network. Information sharing in a cooperative network further results in improvement in the proportional accuracy of the nodes in cases where GNSS is available to all nodes. This study investigated a mathematical model and operational framework for cooperative localization of UAVs using GNSS, microelectromechanical systems (MEMS), inertial navigation system (INS), and UWB (ultra-wide-band) sensors under different architectures. This paper briefly discusses the practical feasibility of different distributed architectures and provides a comparison of distributed and centralized architectures. The study analyzed the proposed network using numerical simulation and investigated changes in performance with respect to different parameters. The simulation results show that the centralized architecture generally provided higher localization accuracy compared with the distributed architecture. It was also observed that reliable and consistent localization can be achieved, irrespective of the size of the network, by using a cooperative approach even if only four nodes have GNSS access in the network if there is good connectivity among the nodes. Further, the simulation results demonstrate that a cooperative approach benefits all the nodes in terms of improved localization accuracy even if all the nodes have access to GNSS.

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 in GNSS-denied environments. This data set was collected as part of a benchmarking measurement campaign carried out at the Ohio State University in October 2017. Pedestrians were equipped with a variety of sensors, including two different UWB systems, on a specially designed helmet serving as a mobile multi-sensor platform for CP. Different users were walking in stop-and-go mode along trajectories with predefined checkpoints and under various challenging environments. In the developed CP network, both Peer-to-Infrastructure (P2I) and Peer-to-Peer (P2P) measurements are used for positioning of the pedestrians. It is realised that the proposed system can achieve decimetre-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 geometry is good. In the absence of these good conditions, the results show that the average accuracy degrades to meter level. Further, it is experimentally demonstrated that inclusion of P2P cooperative range observations further enhances the positioning accuracy and, in extreme cases when only one infrastructure measurement is available, P2P CP may reduce positioning errors by up to $95\%$ . The complete test setup, the methodology for development, and data collection are discussed in this paper. In the next version of this system, additional observations such as the Wi-Fi, camera, and other signals of opportunity will be included.

Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. This paper reports about a sequence of extensive experiments, conducted at The Ohio State University (OSU) as part of the joint effort of the FIG/IAG WG on Multi-sensor Systems. Their overall aim is to assess the feasibility of achieving GNSS-like performance for ubiquitous positioning in terms of autonomous, global, preferably infrastructure-free positioning of portable platforms at affordable cost efficiency. In the data acquisition campaign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were used whereby cooperative positioning (CP) is the major focus to achieve a joint navigation solution. The GPSVan of The Ohio State University was used as the main reference vehicle and for pedestrians, a specially designed helmet was developed. The employed/tested positioning techniques are based on using sensor data from GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), vison-based positioning with cameras and Light Detection and Ranging (LiDAR) as well as inertial sensors. The experimental and initial results include the preliminary data processing, UWB sensor calibration and Wi-Fi indoor positioning with room-level granularity and platform trajectory determination. The results demonstrate that CP techniques are extremely useful for positioning of platforms navigating in swarms or networks. A significant performance improvement in terms of positioning accuracy and reliability is achieved. Using UWB, decimeter-level positioning accuracy is achievable under typical conditions, such as normal walls, average complexity buildings, etc. Using Wi-Fi fingerprinting, success rates of approximately 97 % were obtained for correctly detecting the room-level location of the user.

The fundamental limitation for laser scanning of an object is that the object should be stationary. In this letter, we propose a novel technique for obtaining 3-D point cloud of a moving object by applying motion correction, so that the point cloud is correct in relative and absolute sense, and represents the true shape of the scanned object. In the proposed technique, a laser scanner is used to scan an object which is mounted with a GPS/IMU system and stationed at a distance from the scanner. The object is put in continuous motion (in a limited area) which may be linear, nonlinear, rotational, or of random type. The point cloud collected by the laser scanner is combined with the position and orientation information of the moving object to transform the whole point cloud into a new coordinate system, called object body coordinate system (OBCS), which is fixed to the object. This is realized, first by determining the relationship between an inertial measurement unit (IMU)body coordinate system and the OBCS, and then by using this relation to convert the coordinates of object measured in global coordinate system through laser scanner into the OBCS. Prior to this, the laser scanner is oriented to measure coordinates in global coordinate system. This letter first presents a complete theoretical background of the proposed method for a general case, and then outlines some simplified cases as encountered in practice. The proposed technique has applications in scanning objects that are not stable, e.g., a ship in a harbor.

Precise localization is one of the key requirements in the deployment of UAVs (Unmanned Aerial Vehicles) for any application including precision mapping, surveillance, assisted navigation, search and rescue. The need for precise positioning is even more relevant with the increasing automation in UAVs and growing interest in commercial UAV applications such as transport and delivery. In the near future, the airspace is expected to be occupied with a large number of unmanned as well as manned aircraft, a majority of which are expected to be operating autonomously. This paper develops a new cooperative localization prototype that utilizes information sharing among UAVs and static anchor nodes for precise positioning of the UAVs. The UAVs are retrofitted with low-cost sensors including a camera, GPS receiver, UWB (Ultra Wide Band) radio and low-cost inertial sensors. The performance of the low-cost prototype is evaluated in real-world conditions in partially and obscured GNSS (Global Navigation Satellite Systems) environments. The performance is analyzed for both centralized and distributed cooperative network designs. It is demonstrated that the developed system is capable of achieving navigation grade (2–4 m) accuracy in partially GNSS denied environments, provided a consistent communication in the cooperative network is available. Furthermore, this paper provides experimental validation that information sharing is beneficial to improve positioning performance even in ideal GNSS environments. The experiments demonstrate that the major challenges for low-cost cooperative networks are consistent connectivity among UAV platforms and sensor synchronization.