Most current research on dynamic visual Simultaneous Localization and Mapping (SLAM) systems focuses on scenes where static objects occupy most of the environment. However, in densely populated indoor environments, the movement of the crowd can lead to the loss of feature information, thereby diminishing the system’s robustness and accuracy. This paper proposes a visual SLAM algorithm for dense crowd environments based on a combination of the ORB-SLAM2 framework and RGB-D cameras. Firstly, we introduced a dedicated target detection network thread and improved the performance of the target detection network, enhancing its detection coverage in crowded environments, resulting in a 41.5% increase in average accuracy. Additionally, we found that some feature points other than humans in the detection box were mistakenly deleted. Therefore, we proposed an algorithm based on standard deviation fitting to effectively filter out the features. Finally, our system is evaluated on the TUM and Bonn RGB-D dynamic datasets and compared with ORB-SLAM2 and other state-of-the-art visual dynamic SLAM methods. The results indicate that our system’s pose estimation error is reduced by at least 93.60% and 97.11% compared to ORB-SLAM2 in high dynamic environments and the Bonn RGB-D dynamic dataset, respectively. Our method demonstrates comparable performance compared to other recent visual dynamic SLAM methods.

Under the framework of event‐triggered transmission mechanism, the problem of attack detection and state estimation of multi‐sensor linear time‐invariant systems under static attacks is considered. First, for each transmission channel, the sensor collects measurement information according to an event‐triggered mechanism to reduce unnecessary energy consumption. Then, inspired by the clustering algorithm in machine learning, a detection mechanism based on Gaussian mixture model, which can set a confidence level for the measurement of each sensor is proposed. Finally, centralised data fusion is performed according to the results of attack detection and event‐triggered judgement to realise remote state estimation. A numerical example proves that the proposed algorithm can locate the damaged sensor, save the network transmission bandwidth under the premise of ensuring accuracy and efficiency of sensor estimation.

We consider the secure state estimation of linear time-invariant Gaussian systems subject to dynamic malicious attacks. An error compensator is proposed to reduce the impact of local error data on state estimation. Based on that, a new estimation algorithm based on the Gaussian mixture model (GMM) aiming at dynamic attacks is proposed, which can cluster the local state estimates autonomously and improve the remote estimation accuracy effectively. The superiority of the proposed algorithm is verified by numerical simulations.

Simultaneous Localization and Mapping is an essential element of robot position perception and environmental reconstruction. However, many existing SLAM systems rely on the static environment assumption and have limited adaptability to dynamic crowd scenarios. This article suggests a dynamic SLAM approach that Visual odometry and keyframe improve-ments, respectively to enhance the efficiency and resilience of SLAM systems in dynamic crowds environments. Firstly, a crowd environment-based object detection module is proposed, mainly detecting moving people in the scene and performing dynamic segmentation. Secondly, an adaptive keypoint criterion is designed to achieve further dynamic point detection and improve camera pose. Finally, the pose estimation error is evaluated on TUM and Bonn dynamic datasets. Results show that, compared to ORB-SLAM2, the proposed method achieves the greatest improvement of up to 98.14% in the high dynamic sequence of the TUM dataset. The improvement on Bonn dynamic dataset is at least 95.05%. This suggests the suggested approach possesses strong accuracy and robustness.