一种低数据传输的多机器人实时视觉SLAM

针对多机器人视觉SLAM在实际应用中带宽受限的问题,设计了一种低数据传输的多机器人实时视觉SLAM系统.系统中引入了NetVLAD神经网络模型,通过改进NetVLAD降低了多机器人回环检测的计算资源占用,提高了回环检测的实时性.提出了一种针对描述子缺失情况下的特征匹配算法,提高了回环检测与相对量测的鲁棒性,并提出了一种增量式多机器人位姿图共享和优化方法.最后,通过在KITTI数据集进行测试,验证了该SLAM系统能有效减少多机器人通信过程中的数据传输,具有与单机器人SLAM相当的定位精度和实时性.     

基于因子图的状态估计方法运算实时性研究CSCD

对因子图算法及其理论基础进行研究,使用消元算法分析了一元观测因子图模型的稀疏结构,得到了一种适用于该模型的增量推断算法;并对固定滞后平滑算法进行了研究,实现了滑窗在该增量推断算法中的应用;最后通过仿真验证了基于因子图的状态估计方法的估计能力、该增量推断算法在求解一元观测因子图模型的状态估计问题时运算速度的提升能力,以及滑窗维持该增量推断算法运算速度稳定的能力。     

二维耦合天线阵图结构模型在波束转换和失效分析中的应用

提出了耦合振荡器阵列的一种图结构模型，利用该模型对耦合振荡器阵列有源天线阵的波束转换时间、振荡器失效和耦合网络失效引起的阵列失效概率进行了分析和仿真。从分析结果看，阵列耦合网络具有一定冗余性的设计可以缩短阵列波束转换时间，提高阵列的可靠性。     

A novel combination belief rule base model for mechanical equipment fault diagnosis

Due to the excellent performance in complex systems modeling under small samples and uncertainty, Belief Rule Base(BRB) expert system has been widely applied in fault diagnosis. However, the fault diagnosis process for complex mechanical equipment normally needs multiple attributes, which can lead to the rule number explosion problem in BRB, and limit the efficiency and accuracy. To solve this problem, a novel Combination Belief Rule Base(C-BRB) model based on Directed Acyclic Graph(DAG) structu...     

A model-free method for attitude estimation and inertial parameter identification of a noncooperative target

Determining the attitude and inertial parameters of a noncooperative target is essential in an on-orbit servicing mission. Various methods based on machine vision have been proposed, but most of them require the 3D model of the target. This paper proposes a model-free method through sequentially registering point clouds captured by a depth camera. Our main contributions are the avoidance of the ambiguity in registration, and the combination of the multiplicative extended Kalman filter and the pose graph optimization to reduce the effect of measurement noise and drift error. A hardware experiment was performed to generate the sequence of point clouds of a three-axis free-floating target and validate our method. The result shows that the proposed method outperforms existing methods and effectively identifies the inertial parameters, including the normalized principal moments of inertia and the orientation of principal axes.