Abstract
In this paper, we propose a simultaneous intrinsic and extrinsic parameter identification of a hand-mounted laser-vision sensor (HMLVS). A laser-vision sensor (LVS), consisting of a camera and a laser stripe projector, is used as a sensor component of the robotic measurement system, and it measures the range data with respect to the robot base frame using the robot forward kinematics and the optical triangulation principle. For the optimal estimation of the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. Best-fit parameters, including both the intrinsic and extrinsic parameters of the HMLVS, are simultaneously obtained based on the least-squares criterion. From the simulation and experimental results, it is shown that the parameter identification problem considered was characterized by a highly multimodal landscape; thus, the global optimization technique such as a particle swarm optimization can be a promising tool to identify the model parameters for a HMLVS, while the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum. The proposed optimization method does not require good initial guesses of the system parameters to converge at a very stable solution and it could be applied to a kinematically dissimilar robot system without loss of generality.
Highlights
To measure the range data of objects under an unknown working environment, a range sensing device has been widely applied to various robotic applications [1,2,3,4,5,6]
In this paper we have proposed a new approach to the problem of a hand-mounted laser-vision sensor system calibration based on a particle swarm optimization
The laser-vision sensor, consisting of a camera with a nonlinear radial lens distortion and a laser stripe generator, was used as a sensor module of the robotic measurement system to measure the range data of an object in the robot base coordinate system; and it was modeled based on the forward kinematics and the optical triangulation principle
Summary
To measure the range data of objects under an unknown working environment, a range sensing device has been widely applied to various robotic applications [1,2,3,4,5,6]. A laser-vision sensor (LVS) consisting of a CCD camera(s) and a laser stripe projector has been frequently used as an active ranging device [1,2,3,4,5,6,7,8,9] and a feature detection sensor [10] It is mathematically modeled based on an optical triangulation principle [2] or a conversion matrix [3]. The extrinsic parameters of the omnidirectional laser-vision sensor used in a free-ranging robot were identified after solving the intrinsic parameters based on the existing camera calibration method [3]. The extrinsic parameters were identified first to estimate the orientation and position of a camera with respect to a laser range finder and the camera intrinsic calibration was performed [5].
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