Autonomous landing is a critical step in unmanned aerial vehicles(UAVs)and requires accurate position information. In cases where GPS signals are unavailable or obstructed, vision-based approaches can provide support for landing capabilities. In this study, a vision-based position estimation algorithm is being developed in conjunction with markers used in vertical take-off and landing (VTOL) systems of UAVs. The developed framework is designed to be compatible with various types of visual markers. TheKalman Filter is used to the calculated position to correct measurement errors and reduce the uncertainty of the position estimation. The developedalgorithm is extensively tested in a simulation environment. The positions of a quadrotor aircraft are compared with real measurements to analyze the performance of the proposed vision-based position estimation algorithm. The results demonstrate an acceptable level of accuracy for the algorithm.This study discusses the potential of using visual markers and integrating Kalman filtering to improve the accuracy of positioning in vertical takeoff and landing systemsof the UAVs. The development of a vision-based position estimation algorithm can enhance the reliability and precision of autonomous landing capabilities and enable successful landings in situations where GPS signals are limited or unavailable.Autonomous landing is a critical step in unmanned aerial vehicles(UAVs)and requires accurate position information. In cases where GPS signals are unavailable or obstructed, vision-based approaches can provide support for landing capabilities. In this study, a vision-based position estimation algorithm is being developed in conjunction with markers used in vertical take-off and landing (VTOL) systems of UAVs. The developed framework is designed to be compatible with various types of visual markers. TheKalman Filter is used to the calculated position to correct measurement errors and reduce the uncertainty of the position estimation. The developedalgorithm is extensively tested in a simulation environment. The positions of a quadrotor aircraft are compared with real measurements to analyze the performance of the proposed vision-based position estimation algorithm. The results demonstrate an acceptable level of accuracy for the algorithmAutonomous landing is a critical step in unmanned aerial vehicles(UAVs)and requires accurate position information. In cases where GPS signals are unavailable or obstructed, vision-based approaches can provide support for landing capabilities. In this study, a vision-based position estimation algorithm is being developed in conjunction with markers used in vertical take-off and landing (VTOL) systems of UAVs. The developed framework is designed to be compatible with various types of visual markers. TheKalman Filter is used to the calculated position to correct measurement errors and reduce the uncertainty of the position estimation. The developedalgorithm is extensively tested in a simulation environment. The positions of a quadrotor aircraft are compared with real measurements to analyze the performance of the proposed vision-based position estimation algorithm. The results demonstrate an acceptable level of accuracy for the algorithm.This study discusses the potential of using visual markers and integrating Kalman filtering to improve the accuracy of positioning in vertical takeoff and landing systemsof the UAVs. The development of a vision-based position estimation algorithm can enhance the reliability and precision of autonomous landing capabilities and enable successful landings in situations where GPS signals are limited or unavailable..This study discusses the potential of using visual markers and integrating Kalman filtering to improve the accuracy of positioning in vertical takeoff and landing systemsof the UAVs. The development of a vision-based position estimation algorithm can enhance the reliability and precision of autonomous landing capabilities and enable successful landings in situations where GPS signals are limited or unavailable