With the development of robotics, the Multiple Mobile Robots System (MMRS) has been an attention-getting subject in recent years and has been widely used in military, civilian and other fields. Cooperative localization is the key technology of the MMRS. And observability analysis for MMRS and nonlinear problem are the foremost problems to cooperative localization. In order to enhance the accuracy of cooperative localization for MMRS, a novel cooperative localization algorithm based on hybrid topology architecture under the framework of Extended Kalman Filter (EKF) is proposed in this paper. In this novel algorithm, a hybrid topology architecture based on the Relative Position Measurement Gragh (RPMG) method is adopted to enhance the usage efficiency of observations, improving the cooperative localization accuracy. Meanwhile, EKF is used in this novel cooperative localization algorithm to solve the nonlinearity of the MMRS. Finally, a real dataset is used to verify the availability and superiority of this novel algorithm.

Purpose: The purpose of this paper is to propose a binocular visual odometry algorithm based on the Random Sample Consensus (RANSAC) in visual navigation systems. Design/methodology/approach: The authors propose a novel binocular visual odometry algorithm based on features from accelerated segment test (FAST) extractor and an improved matching method based on the RANSAC. Firstly, features are detected by utilizing the FAST extractor. Secondly, the detected features are roughly matched by utilizing the distance ration of the nearest neighbor and the second nearest neighbor. Finally, wrong matched feature pairs are removed by using the RANSAC method to reduce the interference of error matchings. Findings: The performance of this new algorithm has been examined by an actual experiment data. The results shown that not only the robustness of feature detection and matching can be enhanced but also the positioning error can be significantly reduced by utilizing this novel binocular visual odometry algorithm. The feasibility and effectiveness of the proposed matching method and the improved binocular visual odometry algorithm were also verified in this paper. Practical implications: This paper presents an improved binocular visual odometry algorithm which has been tested by real data. This algorithm can be used for outdoor vehicle navigation. Originality/value: A binocular visual odometer algorithm based on FAST extractor and RANSAC methods is proposed to improve the positioning accuracy and robustness. Experiment results have verified the effectiveness of the present visual odometer algorithm.

The Multiple Mobile Robot (MMR) cooperative system is becoming a focus of study in various fields due to its advantages, such as high efficiency and good fault tolerance. However, the uncertainty and nonlinearity problems severely limit the cooperative localization accuracy of the MMR system. Thus, to solve the problems mentioned above, this manuscript presents a cooperative localization algorithm for MMR systems based on Cubature Kalman Filter (CKF) and adaptive Variance Component Estimation (VCE) methods. In this novel algorithm, a nonlinear filter named CKF is used to enhance the cooperative localization accuracy and reduce the computational load. On the other hand, the adaptive VCE method is introduced to eliminate the effects of unknown system noise. Furthermore, the performance of the proposed algorithm is compared with that of the cooperative localization algorithm based on normal CKF by utilizing the real experiment data. In addition, the results demonstrate that the proposed algorithm outperforms the CKF cooperative localization algorithm both in accuracy and consistency.