This study investigates the current knowledge of UAV-based simultaneous localization and mapping (SLAM) in outdoor environments and discusses challenges and limitations in this field. A systematic scoping review was conducted to identify key concepts, applications, and research gaps in algorithm-oriented and task-oriented, open-source studies. A total of 97 studies met the criteria after a two-step screening. These studies were classified into two main categories: algorithm-oriented and task-oriented. The analysis revealed that most studies focused on the development and implementation of new algorithms. The review highlights the significance and diversity of sensors used in UAVs for different tasks and applications. The evaluation method allows showing the real results and performance of new algorithms in target scenarios compared to public data sets and simulation platforms. SLAM is a fundamental task in robotics, enabling robots to navigate autonomously in unknown or changing environments. SLAM algorithms use sensor data to create maps and estimate the robot's position and orientation. The theoretical basis for SLAM was established by Smith and Cheeseman in 1986. SLAM has been widely used in robotics, particularly in environments where GPS is unavailable. UAV-based SLAM faces challenges such as varying application scenarios, different types of sensors, and evaluation methods due to the limited payload capacity and high flexibility of UAVs. The study defines three research questions: (1) What is the contribution of UAV-based SLAM in outdoor environments? (2) What is the framework of UAV-based SLAM, including sensors, platforms, algorithms, and evaluation methods? (3) What are the challenges, research gaps, and potential uses of UAV-based SLAM in outdoor environments? The review discusses the results according to these questions. Section 2 provides an overview of SLAM, including sensors, odometry, back-end optimization, loop closure, and mapping. Section 3 describes the methodology used in the review. Section 4 presents the quantitative results from the selected literature. Section 5 discusses the results according to the study's research questions. Section 6 concludes the paper. The review highlights the importance of sensors, the evolution of SLAM methods, and the challenges in UAV-based SLAM. It also discusses the application scenarios, experimental methods, hardware, and SLAM algorithms used in UAV-based SLAM. The study concludes that UAV-based SLAM has significant potential but faces challenges that need to be addressed.This study investigates the current knowledge of UAV-based simultaneous localization and mapping (SLAM) in outdoor environments and discusses challenges and limitations in this field. A systematic scoping review was conducted to identify key concepts, applications, and research gaps in algorithm-oriented and task-oriented, open-source studies. A total of 97 studies met the criteria after a two-step screening. These studies were classified into two main categories: algorithm-oriented and task-oriented. The analysis revealed that most studies focused on the development and implementation of new algorithms. The review highlights the significance and diversity of sensors used in UAVs for different tasks and applications. The evaluation method allows showing the real results and performance of new algorithms in target scenarios compared to public data sets and simulation platforms. SLAM is a fundamental task in robotics, enabling robots to navigate autonomously in unknown or changing environments. SLAM algorithms use sensor data to create maps and estimate the robot's position and orientation. The theoretical basis for SLAM was established by Smith and Cheeseman in 1986. SLAM has been widely used in robotics, particularly in environments where GPS is unavailable. UAV-based SLAM faces challenges such as varying application scenarios, different types of sensors, and evaluation methods due to the limited payload capacity and high flexibility of UAVs. The study defines three research questions: (1) What is the contribution of UAV-based SLAM in outdoor environments? (2) What is the framework of UAV-based SLAM, including sensors, platforms, algorithms, and evaluation methods? (3) What are the challenges, research gaps, and potential uses of UAV-based SLAM in outdoor environments? The review discusses the results according to these questions. Section 2 provides an overview of SLAM, including sensors, odometry, back-end optimization, loop closure, and mapping. Section 3 describes the methodology used in the review. Section 4 presents the quantitative results from the selected literature. Section 5 discusses the results according to the study's research questions. Section 6 concludes the paper. The review highlights the importance of sensors, the evolution of SLAM methods, and the challenges in UAV-based SLAM. It also discusses the application scenarios, experimental methods, hardware, and SLAM algorithms used in UAV-based SLAM. The study concludes that UAV-based SLAM has significant potential but faces challenges that need to be addressed.