This paper presents a novel method for extrinsic parameters estimation of a single line scan LiDAR and a camera. Using a checkerboard, the calibration setup is simple and practical. Particularly, the proposed calibration method is based on resolving geometry of the checkerboard that visible to the camera and the LiDAR. The calibration setup geometry is described by planes, lines and points. Our novelty is a new hypothesis of the geometry which is the orthogonal distances between LiDAR points and the line from the intersection between the checkerboard and LiDAR scan plane. To evaluate the performance of the proposed method, we compared our proposed method with the state of the art method i.e. Zhang and Pless [1]. The experimental results showed that the proposed method yielded better results.

This paper presents a supervised foreground segmentation method that uses local and global feature similarity with edge constraint. This framework integrates and extends the notion of region growing and classification to deal with local and global fitness. It parameterizes constraint of growing using Chebyshev's inequality. The constraint is used to stop segmentation before matting. Matting relies on both local and global information. The proposed method outperforms many of the current methods in the sense of correctness and minimal user interaction, and it does so in a reasonable computation time.

This paper presents visual perception discovered in high-level manipulator planning for a robot to reproduce the procedure involved in human painting. First, we apply a technique of 2D object segmentation that considers region similarity as an objective function and edge as a constraint with artificial intelligent used as a criterion function. The system can segment images more effectively than most of existing methods, even if the foreground is very similar to the background. Second, we propose a novel color perception model that shows similarity to human perception. The method outperforms many existing color reduction algorithms. Third, we propose a novel global orientation map perception using a radial basis function. Finally, we use the derived model along with the brush's position- and force-sensing to produce a visual feedback drawing. Experiments show that our system can generate good paintings including portraits.

This paper presents visual perception discovered in high-level manipulator planning for a robot to reproduce the procedure involved in human painting. First, we propose a technique of 3D object segmentation that can work well even when the precision of the cameras is inadequate. Second, we apply a simple yet powerful fast color perception model that shows similarity to human perception. The method outperforms many existing interactive color perception algorithms. Third, we generate global orientation map perception using a radial basis function. Finally, we use the derived foreground, color segments, and orientation map to produce a visual feedback drawing. Our main contributions are 3D object segmentation and color perception schemes.

This paper presents a method to optimize and filter trajectories generated from recorded human motion for a humanoid robot with physical limits. The objective function is responsible for mimicking human trainers, enhancing the possibility for fast convergence, while constraints are used to transform motion within the limit of the capabilities of the humanoid robot. Those constraints for angle, velocity, and dynamic force are represented as B-spline coefficients. An iterative soft-constraint paradigm is proposed to enhance the quality of velocity and force constraints. Collision avoidance is also considered as a constraint. For precision refinement, in regions of high-frequency motion not adequately modeled by an initial splining, B-spline is extensible into a hierarchy so that optimization that meets global criteria can be performed locally. Furthermore, all of the constraints can be used solely as filtering. To use these filters, an effective method to directly decompose a trajectory to a B-spline is also presented

From recorded human motion, trajectory optimization of a humanoid robot with physical limits being the key constraint is presented. The optimization's objective function preserves the salient characteristics of the original motion, while constraints are used to transform the motion to the limit of the capabilities of the humanoid robot. It is shown that using constraints to limit generated trajectories to physically realizable motion ensures that limits are being met more precisely than by reducing them using a standard objective function. The use of wavelets vs. B-spline is compared, and it is shown that B-spline data representation has pronounced advantages. Furthermore, in regions of high-frequency motion not adequately modeled by an initial splining, B-spline is extensible into a hierarchy so that optimizations can be performed locally which still meet global criteria. It is shown how to use B-spline coefficients in angle-, velocity-, acceleration-, and dynamic force-constraints. To detect trajectory subsections with excessive error in need of control point adjustment and local re-optimization, not only is a traditional error detector used, but also a B-spline density detector is also presented. Generated motions were tested using our simulation program and the robot HRP-2.