Although cooperative localization is fundamental to multi-robot systems, current algorithms suffer from the tracking of interdependencies, information fusion from multiple sources, and restriction to specific measurement models. To improve the accuracy of localization algorithms for multi-robot systems and reduce the impact of uncertainty in multi-source measurement information, this paper proposes a resilient decentralized cooperative localization algorithm. We modify the measurement update procedure of the traditional decentralized cooperative localization algorithm to track inter-robot correlations and ensure the independence of the measurement update procedure of the elemental filters. We use optimal information fusion algorithms to fuse multi-source information, and determine the overall estimate of every robot through a weighted sum of multi-source estimates, thereby achieving accurate localization. To enhance the robustness of the multi-robot localization system, an online validation module is added to validate the multi-source estimates. The proposed cooperative localization framework is decentralized and not restricted to specific models. Simulations results show that the proposed algorithm improves localization accuracy and resilience of the multi-robot system compared to existing cooperative localization algorithms. Experimental results using real-world dataset demonstrate that our proposed algorithm can achieve a localization accuracy with an average ARMSE of 0.68 m, and it is 34% better than that of the traditional DCL algorithm.

Traditional Global Navigation Satellite Systems (GNSS) experience their limitations in urban canyons. However, it is significant to improve the accuracy of positioning with the rapid development of smart cities. To solve this problem, a UGV‐UAV robust cooperative positioning algorithm with object detection is proposed, which utilises an unmanned aerial vehicle (UAV) to assist an unmanned ground vehicle (UGV) to achieve accurate positioning. When the UAV is in the sky with a good reception of satellite signals, the UGV uses the YOLOv3 object detection method to detect the UAV in images captured by camera, and acquires visual measurements including angles and ranges of the ground camera relative to the UAV through the proposed monocular vision measuring with object detection (ODMVM) model. Then, in order to solve the problem that visual measurement is disturbed by the real world, a robust Kalman filter is introduced that integrates measurements from available GNSS, inertial measurement unit (IMU), monocular camera, and the position broadcast of cooperative UAV to obtain more robust and accurate position estimation. Experimental and simulation results show that the proposed cooperation positioning algorithm can improve the positioning accuracy by 73.63% compared with the traditional cooperation positioning algorithm in urban canyons.

With the advancement of intelligent perception in multirobot systems, cooperative localization in dynamic environments has become a critical component. However, existing multirobot cooperative localization systems still fall short in meeting high-precision localization requirements in Global Navigation Satellite System (GNSS)-denied environments. In this paper, we propose a factor-graph-based resilient cooperative localization (FG-RCL) algorithm for multirobot systems. This algorithm integrates measurements from visual sensors and Ultra-WideBand (UWB) to achieve accurate cooperative state estimation—overcoming the visibility issues of visual sensors within limited fields of view. We utilize the Joint Probabilistic Data Association (JPDA) algorithm to calculate the corresponding probabilities of multiple visual detection measurements between robots and assign them to their respective edges in the factor graph, thereby addressing the data association challenges in visual detection measurements. Finally, simulation results demonstrate that the proposed algorithm significantly reduces the influence of visual detection measurement interference on the performance of cooperative localization. Experimental results indicate that the proposed algorithm outperforms UWB-based and vision-based methods in terms of localization accuracy. The system is implemented using a factor-graph-based optimization approach, and it exhibits scalability and enables plug-and-play for sensors. Furthermore, it demonstrates resilience in abnormal situations.