Shanping Gao

In the face of large-scale environmental mapping requirements, through the use of lightweight and inexpensive robot groups to perceive the environment, the multi-robot cooperative (V)SLAM scheme can resolve the individual cost, global error accumulation, computational load, and risk concentration problems faced by single-robot SLAM schemes. Such schemes are robust and stable, form a current research hotspot, and relevant algorithms are being updated rapidly. In order to enable the reader to understand the development of this field rapidly and fully, this paper provides a comprehensive review. First, the development history of multi-robot collaborative SLAM is reviewed. Second, the fusion algorithms and architectures are detailed. Third, from the perspective of machine learning classification, the existing algorithms in this field are discussed, including the latest updates. All of this will make it easier for readers to discover problems that need to be studied further. Finally, future research prospects are listed.

Based on cubic B-Spline curve mathematical properties, theoretical analysis the cubic B-Spline curve recursive formula of Taylor development of first-order, derivation of two order in the interpolation cycle under the condition of certain interpolation increment only and interpolation speed, change the interpolation increments can be amended cubic times B-Spline curves purpose. So this paper presents such a modified interpolation algorithm, which is combined with specific examples of matlab7.0 verifies that the algorithm is correct; it is consistent with a cubic B-Spline curve interpolation requirements.

Because of the broad application of human action recognition technology, action recognition has always been a hot spot in computer vision research. The Long Short-Term Memory (LSTM) network is a classic action recognition algorithm, and many effective hybrid algorithms have been proposed based on basic LSTM infrastructure. Although some progress has been made in accuracy, most of those hybrid algorithms have to have more and more complex structures and deeper network levels. After analyzing the structure of the classic LSTM from the perspective of control theory, we determined that the classic LSTM could strengthen the differential characteristics of human action recognition technology to reflect the change of speed. Thus, an improved LSTM structure with an input differential characteristic module is proposed. Furthermore, in this article, we considered the influence of first-order and second-order differential on the extraction of movement pose information, that is, the influence of movement speed and acceleration on action recognition. We designed four different LSTM units with first-order and second-order differential. Moreover, the experiments were performed for the four units on three common datasets repeatedly. We found that the LSTM network with the input differential feature module proposed in this article can effectively improve action recognition accuracy and stability without deepening the complexity of the network and can be used as a new basic LSTM network architecture.

In order to solve the problems of modification algorithm of NURBS curve interpolation, Such as interpolation time bigger, NURBS curve step error and chord error are not easy changed ,and so on.

The algorithm of NURBS interpolation was introduced,it is the key for NURBS curve interpolation that formula of Taylor development of first-order, derivation of two order in the interpolation cycle under the condition of certain interpolation increment only and interpolation speed, by changing the interpolation increments can achieve correction curve of interpolation. This paper introduces a method using DSP motion control card for NURBS curve interpolation methods, this method can not only shorten the interpolation time, also can be controlled by motion control card interrupt timer to adjust interpolation. The last example shows that, the interpolation algorithm not only meet the control requirements, but also meets the requirements of high speed and high precision interpolation.