Optimal Position of Cameras Design in a 4D Foot Scanner

Abstract

Optical motion capturing explains the three-Dimensional (3D) position estimation of points through triangulation employing several depth cameras. Prosperous performance relies on level of visibility of points from different cameras and the overlap of captured meshes in-between. Generally, the accuracy of the estimation is practically based on the camera parameters e.g., location and orientations. Accordingly, the camera network configurations play a key role in the quality of the estimated mesh. This paper proposes an optimal approach for camera placement based on characteristics of a depth camera D435i - Intel RealSense. The optimal problem includes a cost function that contains several minimisation and maximisation terms. The minimisation terms are distance of the cameras to the center of the scanning object, resolution error, and sparsity. And the maximisation terms are distance between each two pair of cameras, percent of captured point from an object, and the level of overlap between cameras. The object is designed based on practical experiments of human walking and is a bounding box around one step of dynamic foot work-space from heel strike posture to toe-off posture. The accuracy and robustness of the algorithms are assessed via experiment measurement, and sensitivity to the number of cameras is investigated. Accordingly, the experiment results determined that the scanning accuracy can be as high as 2.5 % based on a reference scan with a high-end scanner (Artec Eva).

Adaptive time-delay estimation and control of optimized Stewart robot

“…Thus, the optimal problem is finding a set of optimal design parameters l i and u i such thatsubject toand with a conditional constraint ofTwo conditional constraints are proposed to lead the optimization process towards the farthest maximum point ( z = 0.46 m) and farthest minimum point ( z = 0.3 m) locations that the end-effector should travel from its initial location ( z 0 = 0.38 m). By applying the algorithm in Lagarias et al (1998) such as Tajdari et al (2022a), the optimal parameters are designed, and for each of the constraints, the minimum values are obtained, as reported in Table 1(b). Thus, the maximum of the optimal values for each of the parameters is selected to make sure that both the constraint conditions are covered, and then u i = 0.11( m ) and l i = 0.404( m ).…”

Dynamic 3D Mesh Reconstruction Based on Nonrigid Iterative Closest-Farthest Points Registration

“…The concept of body measurement started with using measurement tape, which was time-consuming and required considerable effort [5]. Advancement in technology introduces the 3D scanning tool-kits [6][7][8][9][10][11], which improves the effectiveness and efficiency of measuring the body shapes [12,13]. However, the 3D body measuring techniques [14,15] and scanners only allowed the collecting data in static postures, e.g., hand posture in [16,17].…”