Localization and perception play an important role as the basis of autonomous Unmanned Aerial Vehicle (UAV) applications, providing the internal state of movements and the external understanding of environments. Simultaneous Localization And Mapping (SLAM), one of the critical techniques for localization and perception, is facing technical upgrading, due to the development of embedded hardware, multi-sensor technology, and artificial intelligence. This survey aims at the development of visual SLAM and the basis of UAV applications. The solutions to critical problems for visual SLAM are shown by reviewing state-of-the-art and newly presented algorithms, providing the research progression and direction in three essential aspects: real-time performance, texture-less environments, and dynamic environments. Visual–inertial fusion and learning-based enhancement are discussed for UAV localization and perception to illustrate their role in UAV applications. Subsequently, the trend of UAV localization and perception is shown. The algorithm components, camera configuration, and data processing methods are also introduced to give comprehensive preliminaries. In this paper, we provide coverage of visual SLAM and its related technologies over the past decade, with a specific focus on their applications in autonomous UAV applications. We summarize the current research, reveal potential problems, and outline future trends from academic and engineering perspectives.

Autonomous exploration is a fundamental problem for various applications of unmanned aerial vehicles(UAVs). Existing methods, however, are demonstrated to static local optima and two-dimensional exploration. To address these challenges, this paper introduces GO-FEAP (Global Optimal UAV Planner Using Frontier-Omission-Aware Exploration and Altitude-Stratified Planning), aiming to achieve efficient and complete three-dimensional exploration. Frontier-Omission-Aware Exploration module presented in this work takes into account multiple pivotal factors, encompassing frontier distance, nearby frontier count, frontier duration, and frontier categorization, for a comprehensive assessment of frontier importance. Furthermore, to tackle scenarios with substantial vertical variations, we introduce the Altitude-Stratified Planning strategy, which stratifies the three-dimensional space based on altitude, conducting global-local planning for each stratum. The objective of global planning is to identify the most optimal frontier for exploration, followed by viewpoint selection and local path optimization based on frontier type, ultimately generating dynamically feasible three-dimensional spatial exploration trajectories. We present extensive benchmark and real-world tests, in which our method completes the exploration tasks with unprecedented completeness compared to state-of-the-art approaches.

Quadrotors possess extremely high maneuverability, but this often relies on costly sensors such as radar and depth cameras. In dense and cluttered environments with abnormal lighting conditions, traditional algorithm-based navigation methods face reduced efficiency due to sensor degradation, error accumulation, and processing delays. Learning-based end-to-end vision-control approaches show great potential in such scenarios. However, their performance typically depends on high-quality inputs, such as depth images. In nighttime or cave environments, low-light conditions degrade such information, impairing control effectiveness. This paper presents Night-Flight Vision Transformer (NF-ViT), an end-to-end visual control network designed for low-light environments. By unifying the optimization of low-light enhancement and depth estimation, we reduce sensor requirements to low-light monocular RGB images, improving generalization. Simulations and real-world flight tests demonstrate that NF-ViT outperforms existing methods at speeds up to 7 m/s, representing the first end-to-end visual control network tailored for low-light environments. A demo video is available here: https://www.bilibili.com/video/BV1DqroBUErr/