Multi-view 3D reconstruction technology is used to restore a 3D model of practical value or required objects from a group of images. This paper designs and implements a set of multi-view 3D reconstruction technology, adopts the fusion method of SIFT and SURF feature-point extraction results, increases the number of feature points, adds proportional constraints to improve the robustness of feature-point matching, and uses RANSAC to eliminate false matching. In the sparse reconstruction stage, the traditional incremental SFM algorithm takes a long time, but the accuracy is high; the traditional global SFM algorithm is fast, but its accuracy is low; aiming at the disadvantages of traditional SFM algorithm, this paper proposes a hybrid SFM algorithm, which avoids the problem of the long time consumption of incremental SFM and the problem of the low precision and poor robustness of global SFM; finally, the MVS algorithm of depth-map fusion is used to complete the dense reconstruction of objects, and the related algorithms are used to complete the surface reconstruction, which makes the reconstruction model more realistic.
Inverted pendulum system is a complex, unstable and nonlinear system. In order to make it become an adaptive and robust stable system in control strategy, the mathematical model of linear l-stage inverted pendulum has been established by means of Newton mechanics. The control strategies including classic control methods and modern control methods, such as the PID control algorithm, the pole assignment algorithm and the T-S fuzzy control algorithm.The real-time control results show that the PID control algorithm and the pole assignment algorithm based on the dynamic model have different qualities and characteristic. The structure of PID control is smiple, but the PID controller parameters is more difficult to select, and the overshoot of the system is easy to be increase because of the system strong instability. In addition, the traditional PID control algorithm can only control the pendulum's angle, and can't control displacement. Pole placement method has the better robustness and transient characteristics, but it mainly relies on the experience of engineers to select the desired pole, so it does not have the convenience and simplicity of the PID. The T-S fuzzy controller doesn't need to build an accurate mathematical model of the object, the nonlinear system can be fuzzified into local linera model based on the empirical knowledge. The T-S fuzzy controller reduce the dimension of fuzzy controller and simplify the rules. The real-time control results analysis shows the pendulum and cart position can quickly be stabilized with strong robustness. In real-time control of the inverted pendulum system, car position, car speed and the pendulum angle and angular velocity, etc not only to be considered, but also the state variables, measurement accuracy and sensitivity of the sensor have to consider. So there are a lot of work to do in the real-time control of inverted pendulum system.
The output of ultrasonic object-location meter changes with the environmental temperature. In order to eliminate such a situation, a new information fusion algorithm based on neural network is presented. $ virtual ultrasonic objectlocation measurement temperature compensation system is realized based on a method of the hybrid programming of MATLAB and LabVIEW with MATLAB Script node. A improved BP algorithm ΫΫLM algorithm is used to train the neural network, it can improve the data's convergent speed. Experimental results show that the method can eliminates impacts of temperature to object-location and improves the accuracy of sensor. This virtual object-location measurement system based on BP neural network is of wide and factual application value.
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