LiDAR and UWB-Based Scalable Collaborative Positioning

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.