Artificial Marker and MEMS IMU-Based Pose Estimation Method to Meet Multirotor UAV Landing Requirements

Abstract

The fully autonomous operation of multirotor unmanned air vehicles (UAVs) in many applications requires support of precision landing. Onboard camera and fiducial marker have been widely used for this critical phase due to its low cost and high effectiveness. This paper proposes a six-degrees-of-freedom (DoF) pose estimation solution for UAV landing based on an artificial marker and a micro-electromechanical system (MEMS) inertial measurement unit (IMU). The position and orientation of the landing maker are measured in advance. The absolute position and heading of the UAV are estimated by detecting the marker and extracting corner points with the onboard monocular camera. To achieve continuous and reliable positioning when the marker is occasionally shadowed, IMU data is fused by an extended Kalman filter (EKF). The error terms of the IMU sensor are modeled and estimated. Field experiments show that the positioning accuracy of the proposed system is at centimeter level, and the heading error is less than 0.1 degrees. Comparing to the marker-based approach, the roll and pitch angle errors decreased by 33% and 54% on average. Within five seconds of vision outage, the average drifts of the horizontal and vertical position were 0.41 and 0.09 m, respectively.

Real-Time Monocular Vision System for UAV Autonomous Landing in Outdoor Low-Illumination Environments

“…Since then, various marker-based proposals have been reported, in which distinctive features, such as geometrical shapes, characters, color blocks, and a combination of them have been commonly applied to design landing markers. For instance, Lee et al [ 16 ], Serra et al [ 17 ] and Wu et al [ 18 ] combined black and white square-shaped tags as the landing marker to extract the relative camera position and orientation followed by image-based visual servoing (IBVS) for target tracking. In this case, the square corners are distinctive and robust enough for the visual algorithms to detect.…”

Vector Magnetometry Using Remotely Piloted Aircraft Systems: An Example of Application for Planetary Exploration

“…Using an advance GPS module with Micro-Electro-Mechanical System (MEMS) inertial and pressure sensor technology provides a dynamic accuracy better than 0.3 • in heading, 0.1 • in pitch/roll. This type of IMU has been used for different applications with drones with good results [12,13].…”

“…Using an advance GPS module with Micro-Electro-Mechanical System (MEMS) inertial and pressure sensor technology provides a dynamic accuracy better than 0.3° in heading, 0.1° in pitch/roll. This type of IMU has been used for different applications with drones with good results [12,13]. The power system for the payload and the auxiliary electronics have been allocated in an interface plate attached to the body of the drone.…”

High Precision Positioning with Multi-Camera Setups: Adaptive Kalman Fusion Algorithm for Fiducial Markers

“…Although the fiducial markers were initially developed and used in augmented reality applications [9], due to their versatility in determining their position in a non-invasive manner (by imaging them with a camera), they were quickly adopted for a different range of applications. These include kinematic calibration and visual servoing for industrial robots [10,11], robot localisation and navigation [12][13][14], SLAM (simultaneous localisation and mapping) [15][16][17] and sensor fusion [18][19][20].…”