## 1 Introduction  
Ground and aerial robots are essential for automating diverse civilian and commercial applications, such as search and rescue operations, connected and autonomous driving, precision agriculture, and road traffic management, among others [1; 2]. For example, in the context of new communication technologies, drones may be effectively used as mobile 5G base stations for special events gathering crowds and solving network congestion. Nevertheless, in many of these scenarios, an autonomous robot navigates in a highly dynamic environment and requires precise self-location awareness to operate safely. Thus, localisation can be regarded as the core of autonomous navigation systems. Common localisation systems heavily depend on a Global Navigation Satellite System (GNSS), such as the Global Positioning System (GPS). While it provides sufficient accuracy in most situations, GPS-denied areas might impede or discourage reliance on this technology. Due to the weakness of the GPS signal in indoor environments or forests, cameras, Inertial Measurement Units (IMU) and Light Detection and Ranging (LIDAR) are the principal alternatives to provide valuable information for enabling robot autonomous navigation, which are backed by mature theoretical studies and with many developed solutions. However, each of those sensors presents characteristics that make them unsuitable for specific environments or situations. Vision-based methods provide poor performance in low light, adverse meteorological conditions, or in the presence of visual aliasing. IMU-based localisation suffers from noisy measurements, and motion estimates drift rapidly. LIDAR provides the richest and most precise measurements of the surrounding 3D environment. However, it requires high power consumption when processing large-point cloud data. Moreover, in a wireless sensor network (WSN) with many nodes, GPS is not a reasonable solution regarding cost, power consumption, and form factor requirements of small Internet of Things (IoT) devices [3; 4; 5; 6]. Therefore, multiple and diverse sensors must be integrated and fused to make the system robust. As a complementary solution, a radio frequency (RF) signal can boost localisation accuracy and robustness. Most RF metrics that have been used extensively include Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA), and Received Signal Strength (RSS). Different technologies that can provide these measurements include Wi-Fi, Bluetooth, Ultra-Wideband (UWB), Zigbee, Radio Frequency Identification Devices (RFID), Long-Range Radio (LoRA), and cellular networks [7,8]. Cellular networks are beneficial because we can take advantage of the existing infrastructure, which covers many areas. 5G NR has great potential for enhanced localisation, not only providing accurate measurements and tools based on which even centimetre-level accuracy can be expected, but also enabling the implementation of demanding fusion-based algorithms to compensate for the deficit of each sensor measurement through edge computing. The benefits of 5G NR for localisation include (but are not limited to): wide area coverage, high angular and time resolution measurement for localisation, increased probability of Line of Sight (LOS), and a fast data rate. Concentrating on UAVs and UGVs, 5G can revolutionize localisation by providing:  
* Edge computing. * Vehicle to Everything communication (V2X). * Beamforming. * Multi-array antenna. Several existing surveys exist in the area of localisation covering the topic from different points of view, but they often overlap with each other [7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17]. For instance, [PERSON] et al. [15] explore the positioning in 5G networks from the communication point of view. [PERSON] et al. [9] review the positioning based on RF signal for UAVs, with emphasis on the technologies and challenges, and [PERSON] et al. [8] focus on the IoT technologies and performance metrics. [PERSON] et al.