An efficient vision-based pose estimation algorithm using the assistant reference planes based on the perspective projection rays

• Published in: Sensors and actuators. A. Physical. 2018-04, Vol.272, p.301-309
• Main Authors: Zhang, Zimiao, Zhang, Shihai
• Format: Article
• Language: English
• Subjects: Algorithms; Approximation; Assistant reference planes; Calibration; Cameras; Four coplanar feature points; Mapping; Optical components; Perspective projection rays; Pinholes; Planes; Pose estimation; Projection; Studies
• ISSN: 0924-4247; 1873-3069
• DOI: 10.1016/j.sna.2018.02.003

•Two assistant reference planes based on the perspective projection rays are used to describe the camera model.•The mapping relationship between the image plane and two assistant reference planes are established based on the perspective projection rays.•Four coplanar feature points are used to solve the object pose. The expression of each perspective projection ray could be obtained. The geometrical constraints formed by the four points are then expressed with the perspective projection rays. The vision-based pose estimation using feature point markers is known as the PnP (perspective-n-point) problem. Usually the pose estimation algorithms use the pinhole imaging camera model. However, the traditional pinhole calibration may generate errors. It is a perspective projection algorithm using geometrical approximations to calibrate intrinsic and extrinsic parameters. It does not consider the lens system and only gives a single lumped result for the multiple optical elements. This leads to the accuracy reduction of pose estimation. To solve the problem, the pose estimation algorithm using a new camera model is presented in this paper. Two assistant reference planes are used to describe the camera model. The mapping relationship between the image plane and two assistant reference planes are established based on the perspective projection rays. Four coplanar feature points are used to solve the object pose. The expression of each perspective projection ray could be obtained through the mapping relationship between the image plane and two assistant reference planes. The geometrical constraints formed by the four points are then expressed with the perspective projection rays. The coordinates of feature points in the measurement system are calculated. The object pose is finally obtained. Both noise analysis and accuracy evaluation experiments were used to verify this pose estimation algorithm. Experiment results show that the algorithm in this paper can effectively reduce the influence of the pose estimation data noise and improve the pose estimation accuracy.