Owing to the specific characteristics of Unmanned Aerial Vehicles (UAVs), the demands and applications increase dramatically for them being deployed in confined or closed space for surveying, inspection or detection to substitute human. However, Global Positioning System (GPS) may lose effectiveness or become unavailable due to the potential signal block or interference in that operational environment. Under such circumstances, an imperative requirement on new positioning technology for UAV has emerged. With the rapid development of Radio Frequency (RF) based localisation technologies, leveraging small wireless sensor nodes for low-cost, low latency, low energy consumption and accurate localisation on UAV has received significant attention. However, no up-to-date review has been conducted in this area so far. Therefore, this paper aims to give a comprehensive survey on the RF based localisation systems with different radio communication technologies and localisation mechanisms on UAV positioning. Toward this end, an exhaustive evaluation framework is first established to evaluate the performance of each system on UAV positioning from different perspectives. Particularly, the Ultra-wideband (UWB) based system with time-based mechanisms is highlighted for UAV positioning under the consideration of the proposed evaluation framework. Finally, an intensive analysis is conducted about the current challenges and the potential research issues in this area in order to identify the promising directions for future research.

This paper presents a tag localization algorithm based on the time-difference-of-arrival (TDOA) of mobile tag signal for asynchronous wireless sensor network (WSN) with N anchors (nodes with known locations) and a large number of mobile tags. To obtain time synchronization, all anchors broadcast signals periodically; relative clock offsets and skews of anchor pairs are estimated by the least-square (LS) method using the times-of-arrival (TOAs) of broadcast signals at anchors. When a tag transmits signal, the TOA of tag signal at each anchor is stamped and errors in original TDOAs of tag signal due to relative clock offsets and skews of anchor pairs are eliminated. Based on Gaussian noise model, maximum likelihood estimation (MLE) for the tag position is obtained. Performance issues are addressed by evaluating the Cramér-Rao lower bound of synchronization and localization algorithms. Since the tag can be located via a single transmission, least power consumption of tag is required, and large number of tags can be served in WSN. The proposed algorithm is simple and effective, with performance close to that of synchronous TDOA algorithm.

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In this paper, a high-precision ultra-wideband (UWB) based unmanned aerial vehicle (UAV) localisation approach is proposed for applications in extremely confined environments. It is motivated by the emerging demand on autonomous inspection in such environments that are hard or impossible for humans to access. Instead of the traditional localisation techniques such as global positioning system (GPS), vision based or other localisation techniques, the UWB based localisation technique is adopted for precise UAV positioning due to its high accuracy, implementation simplicity and suitability in such environments. To avoid the requirement on strict synchronisation between sensor nodes and provide decimetre-level accuracy, the proposed algorithm combined the two-way time-of-flight (TW-TOF) localisation scheme with the maximum likelihood estimation (MLE) method. This differs from applications in other environments, the number and deployment area of anchor nodes are highly restricted in such environments. Therefore, an in-depth investigation for the anchor deployment strategies is presented to find the most suitable geometry configurations with accurate and robust performance. Finally, extensive simulations, static experiments and flight tests have been conducted to validate the localisation performance under different deployment strategies. The experiments show that average localisation error and standard deviation (STD) under 0.2 m and 0.07 m are obtainable by using our proposed approach under three different geometry configurations of anchor nodes. This is suitable for different applications in extremely confined environments.

Monocular Visual Simultaneous Localisation and Mapping (VSLAM) systems are widely utilised for intelligent mobile robots to work in unknown environments. However, complex and varying illuminations challenge the accuracy and robustness of VSLAM systems significantly. Existing feature-based VSLAM methods often fail due to the insufficient feature points that can be extracted in those challenging illumination environments. Therefore, this paper proposes an improved ORB-SLAM algorithm based on adaptive FAST threshold and image enhancement (AFE-ORB-SLAM), which works in the environments with complex lighting conditions. An improved truncated Adaptive Gamma Correction (AGC) is combined with unsharp masking to reduce the effect caused by different illuminations. What is more, an improved ORB feature extraction method with the adaptive FAST threshold is proposed and adopted to obtain more reliable feature points. To verify the performance of the AFE-ORB-SLAM, three public datasets (the extended Imperial College London and National University of Ireland Maynooth (ICL-NUIM) dataset with different lighting conditions, Onboard Illumination Visual-Inertial Odometry (OIVIO) dataset and the European Robotics Challenge (EuRoC) dataset) are utilised. The results are compared with other state-of-the-art monocular VSLAM methods. The experimental results demonstrate that the AFE-ORB-SLAM could achieve the highest average localisation accuracy with robust performance in the environments with complex lighting conditions while keeping similar performance in the normal lighting scenarios.