This study aims to investigate the current knowledge of unmanned aerial vehicle (UAV)-based simultaneous localization and mapping (SLAM) in outdoor environments and to discuss the challenges and limitations in this field. A literature search was conducted in three online databases (Web of Science, Scopus, and IEEE) for articles published before October 2022 related to UAV-based SLAM. A scoping review was carried out to identify key concepts and applications, and discover research gaps in the use of algorithm-oriented and task-oriented, open-source studies. A total of 97 studies met the criteria after a two-step screening process. These studies were classified into two main categories: algorithm-oriented studies and task-oriented studies. The analysis revealed that the majority of studies focused on the development and implementation of new algorithms. The review highlights the significance and diversity of sensors utilized in UAVs in different tasks and application scenarios, including LiDAR, monocular cameras, and visual sensors. The evaluation methods show the real results and performance of new algorithms in target scenarios compared with public data sets and simulation platforms. - SLAM - UAVs - Robustness and scalability under long-term autonomy - Metric maps and semantic map models - SLAM combined with deep learning - Novel and unconventional sensors - Sensor selection and usage - Algorithmic challenges and limitations - Task-oriented applications and scenarios - UAV platforms and sensor categories - SLAM algorithms and computational platforms - Open-source studies and their impact - Overview of UAV-based SLAM studies - Sensor usage and limitations - Algorithmic challenges and advancements - Task-oriented applications and scenarios - Recommendations for future research The study provides a comprehensive overview of the current state of UAV-based SLAM in outdoor environments, highlighting the key concepts, applications, and research gaps. It also discusses the challenges and limitations, offering insights for future research and practical applications.This study aims to investigate the current knowledge of unmanned aerial vehicle (UAV)-based simultaneous localization and mapping (SLAM) in outdoor environments and to discuss the challenges and limitations in this field. A literature search was conducted in three online databases (Web of Science, Scopus, and IEEE) for articles published before October 2022 related to UAV-based SLAM. A scoping review was carried out to identify key concepts and applications, and discover research gaps in the use of algorithm-oriented and task-oriented, open-source studies. A total of 97 studies met the criteria after a two-step screening process. These studies were classified into two main categories: algorithm-oriented studies and task-oriented studies. The analysis revealed that the majority of studies focused on the development and implementation of new algorithms. The review highlights the significance and diversity of sensors utilized in UAVs in different tasks and application scenarios, including LiDAR, monocular cameras, and visual sensors. The evaluation methods show the real results and performance of new algorithms in target scenarios compared with public data sets and simulation platforms. - SLAM - UAVs - Robustness and scalability under long-term autonomy - Metric maps and semantic map models - SLAM combined with deep learning - Novel and unconventional sensors - Sensor selection and usage - Algorithmic challenges and limitations - Task-oriented applications and scenarios - UAV platforms and sensor categories - SLAM algorithms and computational platforms - Open-source studies and their impact - Overview of UAV-based SLAM studies - Sensor usage and limitations - Algorithmic challenges and advancements - Task-oriented applications and scenarios - Recommendations for future research The study provides a comprehensive overview of the current state of UAV-based SLAM in outdoor environments, highlighting the key concepts, applications, and research gaps. It also discusses the challenges and limitations, offering insights for future research and practical applications.