Pose Estimation of Omnidirectional Camera with Improved EPnP Algorithm

Facial augmented reality based on hierarchical optimization of similarity aspect graph

BACKGROUND

The existing face matching method requires a point cloud to be drawn on the real face for registration, which results in low registration accuracy due to the irregular deformation of the patient's skin that makes the point cloud have many outlier points.

METHODS

This work proposes a non-contact pose estimation method based on similarity aspect graph hierarchical optimization. The proposed method constructs a distance-weighted and triangular-constrained similarity measure to describe the similarity between views by automatically identifying the 2D and 3D feature points of the face. A mutual similarity clustering method is proposed to construct a hierarchical aspect graph with 3D pose as nodes. A Monte Carlo tree search strategy is used to search the hierarchical aspect graph for determining the optimal pose of the facial 3D model, so as to realize the accurate registration of the facial 3D model and the real face.

RESULTS

The proposed method was used to conduct accuracy verification experiments on the phantoms and volunteers, which were compared with four advanced pose calibration methods. The proposed method obtained average fusion errors of 1.13 ± 0.20 mm and 0.92 ± 0.08 mm in head phantom and volunteer experiments, respectively, which exhibits the best fusion performance among all comparison methods.

CONCLUSIONS

Our experiments proved the effectiveness of the proposed pose estimation method in facial augmented reality.

Line-Features-Based Pose Estimation Method for the Disc Cutter Holder of Shield Machine

To achieve automatic disc cutter replacement of shield machines, measuring the accurate pose of the disc cutter holder by machine vision is crucial. However, under polluted and restricted illumination conditions, achieving pose estimation by vision is a great challenge. This paper proposes a line-features-based pose estimation method for the disc cutter holder of the shield machine by using a monocular camera. For the blurring effect of rounded corners on the image edge, a rounded edge model is established to obtain edge points that better match the 3D model of the workpiece. To obtain the edge search box corresponding to each edge, a contour separation method based on an adaptive threshold region growing method is proposed. By preprocesses on the edge points of each edge, the efficiency and the accuracy of RANSAC linear fitting are improved. The experimental result shows that the proposed pose estimation method is highly reliable and can meet the measurement accuracy requirements in practical engineering applications.

An Accurate and Robust Method for Absolute Pose Estimation with UAV Using RANSAC

In this paper, we proposed an accurate and robust method for absolute pose estimation with UAV (unmanned aerial vehicle) using RANSAC (random sample consensus). Because the artificial 3D control points with high accuracy are time-consuming and the small point set may lead low measuring accuracy, we designed a customized UAV to efficiently obtain mass 3D points. A light source was mounted on the UAV and used as a 3D point. The position of the 3D point was given by RTK (real-time kinematic) mounted on the UAV, and the position of the corresponding 2D point was given by feature extraction. The 2D-3D point correspondences exhibited some outliers because of the failure of feature extraction, the error of RTK, and wrong matches. Hence, RANSAC was used to remove the outliers and obtain the coarse pose. Then, we proposed a method to refine the coarse pose, whose procedure was formulated as the optimization of a cost function about the reprojection error based on the error transferring model and gradient descent to refine it. Before that, normalization was given for all the valid 2D-3D point correspondences to improve the estimation accuracy. In addition, we manufactured a prototype of a UAV with RTK and light source to obtain mass 2D-3D point correspondences for real images. Lastly, we provided a thorough test using synthetic data and real images, compared with several state-of-the-art perspective-n-point solvers. Experimental results showed that, even with a high outlier ratio, our proposed method had better performance in terms of numerical stability, noise sensitivity, and computational speed.

Pose Determination of the Disc Cutter Holder of Shield Machine Based on Monocular Vision

The visual measurement system plays a vital role in the disc cutter changing robot of the shield machine, and its accuracy directly determines the success rate of the disc cutter grasping. However, the actual industrial environment with strong noise brings a great challenge to the pose measurement methods. The existing methods are difficult to meet the required accuracy of pose measurement based on machine vision under the disc cutter changing conditions. To solve this problem, we propose a monocular visual pose measurement method consisting of the high precision optimal solution to the PnP problem (OPnP) method and the highly robust distance matching (DM) method. First, the OPnP method is used to calculate the rough pose of the shield machine’s cutter holder, and then the DM method is used to measure its pose accurately. Simulation results show that the proposed monocular measurement method has better accuracy and robustness than the several mainstream PnP methods. The experimental results also show that the maximum error of the proposed method is 0.28° in the direction of rotation and 0.32 mm in the direction of translation, which can meet the measurement accuracy requirement of the vision system of the disc cutter changing robot in practical engineering application.

State Estimation of Axisymmetric Target Based on Beacon Linear Features and View Relation

In order to realize state estimation for axisymmetric targets and improve the accuracy and robustness of state estimation, a state estimation method for axisymmetric targets based on beacon linear features and view relation is proposed in this paper. The depth camera is used to collect the image and depth information of the object, and the features of the beacon line are extracted by the thinning process and Hough transform. Then, the rotation matrix model based on view relation is constructed to solve the target state. Finally, an axisymmetric shore power plug is taken as the experimental object and the L–V (linear features and view relation) state estimation method is compared with the C–H and C–IPPE state estimation methods. The experimental results show that the L–V state estimation method has higher accuracy and robustness.