Xilong Liu

A Fast Orientation Estimation Approach of Natural Images

This correspondence paper proposes a fast orientation estimation approach of natural images without the help of semantic information. Different from traditional low-level features, our low-level features are extracted inspired by the biological simple cells of the visual cortex. Two approximated receptive fields to mimic the biological cells are presented, and a local rotation operator is introduced to determine the optimal output and local orientation corresponding to an image position, which serve as the low-level feature employed in this paper. To generate the low-level features, a bisection method is applied to the first derivative of the model of receptive fields. Moreover, the feature screener is introduced to eliminate too much useless low-level features, which will speed up the processing time. After all the valuable low-level features are combined, the overall image orientation is estimated. The proposed approach possesses several features suitable for real-time applications. First, it avoids the tedious training procedure of some conventional methods. Second, no specific reference such as the horizon is assumed and no a priori knowledge of image is required. The proposed approach achieves a real-time orientation estimation of natural images using only low-level features with a satisfactory resolution. The effectiveness of our proposed approach is verified on real images with complex scenes and strong noises.

Embedded Vision-Based Autonomous Move-to-Grasp Approach for a Mobile Manipulator

This paper proposes a vision-based autonomous move-to-grasp approach for a compact mobile manipulator under some low and small environments. The visual information of specified object with a radial symbol and an overhead colour block is extracted from two CMOS cameras in an embedded way. Furthermore, the mobile platform and the postures of the manipulator are adjusted continuously by vision-based control, which drives the mobile manipulator approaching the object. When the mobile manipulator is sufficiently close to the object, only the manipulator moves to grasp the object based on the incremental movement with its head end centre of the end-effector conforming to a Bezier curve. The effectiveness of the proposed approach is verified by experiments.

Bezier curve based path planning for a mobile manipulator in unknown environments

This paper presents a collision-free path planning approach based on Bezier curve for a mobile manipulator with the endpoints restricted by the manipulator. Based on these candidate endpoints and the initial posture of mobile manipulator, a series of feasible Bezier paths are obtained with the constraints from velocity, acceleration and environment. And then the optimal collision-free path is determined according to the related information of the path as well as obstacles. The mobile manipulator has the ability to adapt to the environment and the optimal path will be updated once the new detected obstacles block this path. The path planning approach is verified by simulations.

A slope location and orientation estimation method based on 3D LiDAR suitable for quadruped robots

3D LiDAR is widely used for mobile robots environment perception nowadays, which has high precision of distance measurement. In this paper, a calibration method which is suitable for Velodyne VLP-16 is firstly introduced. And then we utilize statistical filtering for isolated points removal. After statistical filtering, a slope location and orientation estimation method is presented based on PROSAC algorithm, which can achieve plane extraction from the 3D point cloud data. Experiments are conducted to testify the performance of the presented method.

Intelligent Line Segment Perception With Cortex-Like Mechanisms

This paper proposes a novel general framework for line segment perception, which is motivated by a biological visual cortex, and requires no parameter tuning. In this framework, we design a model to approximate receptive fields of simple cells. More importantly, the structure of biological orientation columns is imitated by organizing artificial complex and hypercomplex cells with the same orientation into independent arrays. Besides, an interaction mechanism is implemented by a set of self-organization rules. Enlightened by the visual topological theory, the outputs of these artificial cells are integrated to generate line segments that can describe nonlocal structural information of images. Each line segment is evaluated quantitatively by its significance. The computation complexity is also analyzed. The proposed method is tested and compared to state-of-the-art algorithms on real images with complex scenes and strong noises. The experiments demonstrate that our method outperforms the existing methods in the balance between conciseness and completeness.

A general robot environment understanding approach inspired by biological visual cortex

A general vision approach for environment understanding of mobile robot is proposed, which is inspired by biological visual cortex. Gray level image from robot camera is firstly processed in the selective attention layer, and then in S and C layers for meaningful structure elements. With the help of the Sc layer designed for endpoints detection, the line segments map is generated to serve as the form of environment understanding results. Experiments on mobile robot are implemented by the task of avoiding obstacles with different sizes and shapes, pits, sharp cliffs, et al. The experimental results demonstrate the adaptability and versatility of the proposed approach.

Contour Primitives of Interest Extraction Method for Microscopic Images and Its Application on Pose Measurement

This paper proposes a suite of methods to realize high precision pose measurement in 3-D Cartesian space based on a multicamera microscopic vision system. Since it is inefficient to develop a specific image algorithm for each kind of object and the imaging condition might be unsatisfactory, we propose a method of contour primitives of interest extraction, which allows flexible reconfiguration for novel object image and owns robustness under different imaging conditions. The object is detected in grayscale image based on a template of contour primitives. Edges are extracted according to derivatives along the normal vectors of these contour primitives. The positions and directional derivatives of these edges are used for feature extraction and autofocus, respectively. The point features and line features extracted from multiview images are utilized to measure 3-D vectors and orientations, respectively, based on image Jacobian matrices. Cameras’ linear motions are considered in the imaging model, so that the measurement range is expanded beyond the limitation of microscopes’ shallow depths of field. The affine epipolar constraint and focused planes intersection constraint between cameras are applied to improve the real time performances of image feature extraction and multicamera autofocus, respectively. A series of experiments are conducted to verify the effectiveness of the proposed methods. The root mean square errors of pose measurement are evaluated as 3

Feature-Related Searching Control Model for Curve Detection

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Image Dynamics-Based Visual Servoing for Quadrotors Tracking a Target With a Nonlinear Trajectory Observer

in position and 0.05° in orientation, while the measurement range is about 5000

A Robust Detection Method of Control Points for Calibration and Measurement With Defocused Images

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