SUMMARY: A data association algorithm for simultaneous localization and mapping (SLAM) based on central difference joint compatibility (CDJC) criterion and clustering is proposed to obtain the data association results. Firstly, CDJC criterion is designed to calculate joint Mahalanobis distance. Secondly, ordering points to identify the clustering structure is used to divide all observed features into several groups. Thirdly, CDJC branch and bound method is designed to be performed in each group. The results based on simulation data and benchmark dataset show that the proposed algorithm has low computational complexity and provide accurate association results for SLAM of mobile robot.

Existing full-order terminal sliding mode (FOTSM) control methods often require a priori knowledge of the system model. To tackle this problem, two novel neural-network-based FOTSM control methods were proposed. The first one was model based but did not require knowledge of the uncertainties’ bounds. The second one was model free and did not require knowledge of the system model. Finite-time convergence of the two schemes was verified by theoretical analysis and simulation cases. Meanwhile, the designed methods avoided singularity as well as chattering.

In Laser-SLAM system of unmanned delivery vehicle, there are two kinds of association methods applied to solve the data association problem. Compared with the method of independent association for a single measurement and a single feature, the methods of batch association of measurements and features can provide more accurate association results in the state estimation stage of SLAM. In order to obtain a better association solution, a joint data association method based on heuristic search algorithm (HSA-JDA) is proposed to improve the robustness and accuracy of data association. In HSA-JDA, according to the joint maximum likelihood criterion, the data association problem is evolved into a combinatorial optimization problem of how to determine the optimal association set. A heuristic search algorithm that is an optimized artificial fish swarm algorithm by using adaptive step size and adding fish swarm jumping behavior is applied to search the optimal association solution. Experimental results show that HSA-JDA method ensures high association accuracy and then improves the robustness and accuracy of the whole state estimation results of SLAM. It can be used in the Laser-SLAM system based on Kalman filter to provide reliable association results for improving the accuracy of SLAM estimation results for unmanned delivery vehicle.