A Vision-based Method for Autonomous Landing on a Target with a Quadcopter

During autonomous flight procedure, autonomous landing on a specified object is one of the most dangerous and challenging processes, requiring an advanced study in both hardware and software approaches. In this paper, a new methodology is developed, including two major tasks, for vision-based autonomous landing systems executed by a quadcopter: (1) a vision-based algorithm is used to detect and predict an object's future motion using Kalman Filter; (2) PID algorithm is implemented in a quadcopter platform to autonomously balance and land this one on a stationary target. This autonomous task is obtained by two essential components: accelerometer and gyroscope. However, the sensors are susceptible to noise caused by driving forces in the system, such as the vibration of the motors. Therefore, we also investigate a use of complementary filter to make the outcome from two sensors as best as possible. Real quadcopter experiments have been implemented to validate the effectiveness of the proposed method.