## 1 Introduction  
The cost-effectiveness, ease of access, and mission versatility are the primary compelling qualities of UAVs that attract many aerospace and related sectors. Hence, UAVs are being integrated into tasks such as package delivery, first aid, law enforcement, disaster management, infrastructure inspection, agriculture mechanization, rescue, military intelligence, and many more. As low-altitude aerial vehicles, however, UAVs often encounter obstacles such as trees, mountains, high storey buildings, electric poles, and so on during their missions. Therefore, these aerial vehicles should be equipped with sensors to perceive the environment around them and avoid potential dangers. To leverage the use of UAVs in cluttered environments, studies have been conducted on the types and ways of integrating various sensors for autonomous navigation. Vehicle localization is one of the pillars of autonomous navigation. In an open-air space, Global Positioning System (GPS) is often used for UAV localization. However, GPS-based UAV localization in cluttered environment is unreliable. In such environment, sensors onboard the UAV are used for localization as well as collision avoidance. [PERSON] et al. [1] fused inputs from visual camera and Inertial Navigation System (INS) to localize a UAV. Based on computer simulation, the team analyzed various approaches to vision-based UAVposition estimation. [PERSON] et al. [2] combined inputs from GPS, INS, and vision sensors to autonomously navigate UAVs. In their report, the inclusion of GPS input reduces vision-based UAV localization errors and hence enhances the accuracy of navigation. [PERSON] et al. [3] presented a methodology of assessing the performance of sensors fusion for autonomous flight of UAVs. Their methodology systematically analyzes the efficiency of input data for accurate navigation of UAVs. Computer vision technology has evolved over the years to the stage that enables not only UAV localization but also obstacle detection and avoidance. This is realized through the advent of high-performance computers with the ability to process data and perform complex calculations at high speeds. With the promising progress in computer vision technology, many vision-based navigation algorithms have been developing. A comprehensive review of computer vision algorithms and their implementations for UAVs' autonomous navigation was presented by [PERSON] [4]. [PERSON] et al. [5] provided a detailed analysis on the implementation of computer vision technologies for navigation, control, tracking, and obstacle avoidance of UAVs. [PERSON] et al. [6] also explored various computer vision algorithms and their capabilities to detect and track a moving object such as a UAV in flight. Alongside computer vision technology, Artificial Intelligence (AI) is being implemented into UAVs navigation system to enable them to acquire humanoid perception. The idea is to train the computer that is either onboard a UAV or integrated with ground-based command system so that it takes control of UAV navigation with little to no human intervention. [PERSON] and [PERSON] [7] reviewed the historical development of AI and its implementation to UAVs with a particular focus on UAVs control strategies and object recognition for autonomous flight of UAVs. They also considered machine-learning-based UAV path planning and navigation methods. The integration of AI and computer vision technology brings a remarkable importance in civilian application of UAVs. Many challenging tasks such as wildlife monitoring, disaster management, and search and rescue are being addressed by UAVs equipped with AI and computer technology. [PERSON] et al. [8] reported how AI- and computer-vision-enabled UAVs have solved the challenges of wildlife monitoring. The study reported by [PERSON] and [PERSON] [9] reflects the importance of UAVs equipped with AI and computer vision for autonomous monitoring of disaster-stricken areas. [PERSON] et al.