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.

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.

In this study, a robust adaptive filter using fuzzy logic for tightly‐coupled visual‐inertial odometry (VIO) navigation system is proposed. First, the authors use the epipolar geometry and trifocal tensor geometry as the measurement models of the VIO navigation system, which can avoid calculating the three‐dimensional position of feature points. Second, the camera poses corresponding to the three images are corrected in the filter to form a multi‐state constraint Kalman filter. Finally, when the measurement noise statistics changes or the erroneous measurement occurs in practical applications, the proposed method utilises Takagi–Sugeno (T–S) fuzzy logic to determine a scalar factor according to the divergence degree parameter. Then, the scalar factor is applied to the innovation covariance matrix and the filter gain, which improves the navigation accuracy and robustness of the VIO navigation system. The experimental results testing with the publicly available real‐world KITTI dataset demonstrate the effectiveness of the proposed method.

Adaptive antenna arrays have been used to mitigate the interference on global navigation satellite system (GNSS) receivers. The performance of interference mitigation depends on the beamforming algorithms adopted by the antenna array. However, the adaptive beamforming will change the array pattern in realtime, which has the potential to introduce phase center biases into the antenna array. For precise applications, these phase biases must be mitigated or compensated because they will bring errors in code phase and carrier phase measurements. A novel adaptive beamforming algorithm is proposed firstly, then the phase bias induced by the proposed algorithm is estimated, and finally a compensation strategy is addressed. Simulations demonstrate that the proposed beamforming algorithm suppresses effectively the strong interference and improves significantly the capturing performance of GNSS signals. Simultaneously, the bias compensation method avoids the loss of the carrier phase lock and reduces the phase measurement errors for GNSS receivers.

Considering the radio-based indoor positioning system pertaining to signal degradation due to the environmental factors, and rising popularity of IP (Internet Protocol) cameras in cities, a novel fusion of inertial measurement units (IMUs) with external IP cameras to determine the positions of moving users in indoor environments is presented. This approach uses a fine-tuned Faster R-CNN (Region Convolutional Neural Network) to detect users in images captured by cameras, and acquires visual measurements including ranges and angles of users with respect to the cameras based on the proposed monocular vision relatively measuring (MVRM) method. The results are determined by integrating the positions predicted by each user’s inertial measurement unit (IMU) and visual measurements using an EKF (Extended Kalman Filter). The results experimentally show that the ranging accuracy is affected by both the detected bounding box’s by Faster R-CNN height errors and diverse measuring distances, however, the heading accuracy is solely interfered with bounding box’s horizontal biases. The indoor obstacles including stationary obstacles and a pedestrian in our tests more significantly decrease the accuracy of ranging than that of heading, and the effect of a pedestrian on the heading errors is greater than stationary obstacles on that. We implemented a positioning test for a single user and an external camera in five indoor scenarios to evaluate the performance. The robust fused IMU/MVRM solution significantly decreases the positioning errors and shows better performance in dense multipath scenarios compared with the pure MVRM solution and ultra-wideband (UWB) solution.