Chengtao Cai

A Novel Robot Visual Homing Method Based on SIFT Features

Warping is an effective visual homing method for robot local navigation. However, the performance of the warping method can be greatly influenced by the changes of the environment in a real scene, thus resulting in lower accuracy. In order to solve the above problem and to get higher homing precision, a novel robot visual homing algorithm is proposed by combining SIFT (scale-invariant feature transform) features with the warping method. The algorithm is novel in using SIFT features as landmarks instead of the pixels in the horizon region of the panoramic image. In addition, to further improve the matching accuracy of landmarks in the homing algorithm, a novel mismatching elimination algorithm, based on the distribution characteristics of landmarks in the catadioptric panoramic image, is proposed. Experiments on image databases and on a real scene confirm the effectiveness of the proposed method.

An adaptive factor-based method for improving dark channel prior dehazing

The scattering effects of the atmospheric particles in the air affects significantly contrast reduction and color fading. To address this challenging, many attention have been paid to this issue. The foggy image generally contains the sky and non-sky regions while the pixel values in this two distinguished regions is different. The dark channel prior algorithm has been considered as one effective dehazing method which only uses one constant factor for the overall image regardless of the scene pattern. This imprudent procedure results in more darkness image color and fails to accomplish excellent results. In this paper we propose one adaptive factor-based approach to improving dark channel prior dehazing. In our methods, the foggy image is segmented into sky region and non-sky region by Otsu, the critical parameters i.e. light intensity and transmission ratio are obtained based on different factors. Some experiments have been conducted for validating dehazing performance of the proposed approach.

Local environments modelling and path planning for patrol robot in the substation

Substation is the hub of the power grid. Regular inspection is very crucial in order to confirm the electrical equipment in substation to operate normally. Recently, manual inspection is gradually replaced by inspection robot. Local environment modelling and path planning are mainly key problems when patrol robot carry out the inspection work. For dealing with this challenging but imperative issue, there are numerous researchers have strove for this scientific field and have proposed some valuable approaches. The LIDAR is one of excellent sensors for environment perception and collision avoidance for robot. For enhancing the suitability of path planning, a novel local environment modelling method is proposed in which the safety, accessibility, stability and reachability are taken into account when the patrol robot moves in unknown environment, one robot control algorithm which meet the kinematics and dynamics motion principle is investigate as well. Some simulation experiments have also been conducted for validating modelling and planning performance of the proposed approach.

A non-linear combination filtering algorithm in MINS/GNSS navigation system

Considering the nonlinear and uncertainty in the MINS/GNSS navigation system, a nonlinear Sage-Husa noise maximum posterior estimator was designed. Since the estimator cannot solve problems both of system noise and observation, a Bi-parallel BP neural network controller is designed to approximate the estimator. Then an adaptive UKF algorithm based on Bi-parallel neural network is proposed. In the case of uncertain noise, the simulation and analysis were shown that data saturation was emerged in the preceding filtering algorithm. A strong tracking UKF algorithm based on variance inflation factor was combined with the preceding algorithm in some conversion condition. The simulation in the paper was shown that the combined adaptive nonlinear filtering algorithm could suppress divergence and ensure precision.

A novel mismatching elimination algorithm based on distribution of features

Catadioptric panoramic images application to computer visual field gains its popularity in recent years. However, due to its complicated imaging relationship, most existing mismatching elimination algorithms cannot directly operate on the unprocessed panoramic images. Those above algorithms usually need to unwarp the panoramic images before further processing. In order to solve the above problems, based on the distribution characteristics of features in the panoramic image, a novel mismatching elimination algorithm is proposed in this paper. Under different scene conditions, the novel algorithm can eliminate the mismatching features and improve the matching accuracy effectively. Experiments on the image databases confirm its effectiveness.