Abstract
Pose estimation is an important issue for the visual servoing of multicopters. With a process model customized for multicopters and a generic camera model, an extended Kalman filter (EKF) method is proposed in this paper to estimate the pose of multicopters based on off-board multiple cameras. In consideration of EKF implementation, a general point correspondence method that can deal with any number of feature points and cameras is further designed. The proposed EKF method is applicable to not only a single fish-eye (or conventional) camera but also multiple fish-eye (or conventional) cameras. Observability analysis and experiment results show the proposed EKF method is more robust against noise and marker missing than existing filtering methods.
Highlights
With the nonlinear process model customized for multicopters [17] and a generic camera model [18], an extended Kalman filter (EKF) pose estimation method is proposed to perform pose estimation for multicopters based on off-board multiple cameras in this paper
The main contributions are: i) With a generic camera model and a nonlinear process model customized for multicopters, an EKF pose estimation method is proposed, which is applicable to a single fish-eye camera and multiple fish-eye cameras; ii) A general point correspondence method that can deal with any number of feature points and any number of cameras is given; iii) Observability analysis and experimental results demonstrate the proposed EKF method is more robust against noise and marker missing than existing filtering methods
Method; (ii) the traditional EKF method; (iii) a loosely-coupled filtering method least-squares fitting (LSF)+KF, which first estimates the pose by using the LSF method, and refines it using a Kalman filter with the process model (6)
Summary
Multicopters are used in many applications. Pose estimation is known to be very important for autonomous control of these vehicles [1], [2]. In order to deal with marker missing, this paper chooses to: i) use large-FOV fisheye cameras [6]; ii) design a new method that can estimate the pose of multicopters when less markers are observed. The main contributions are: i) With a generic camera model and a nonlinear process model customized for multicopters, an EKF pose estimation method is proposed, which is applicable to a single fish-eye (or conventional) camera and multiple fish-eye (or conventional) cameras; ii) A general point correspondence method that can deal with any number of feature points and any number of cameras is given; iii) Observability analysis and experimental results demonstrate the proposed EKF method is more robust against noise and marker missing than existing filtering methods.
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