Reliable state estimation is a prerequisite for autonomous robot navigation in complex environments. In this work, we present LIO-Fusion, a reinforced LiDAR inertial odometry system that optimally fuses GNSS/relocalization and wheel odometry to provide accurate and robust 6-DoF movement estimation under challenging perceptual conditions. LIO-Fusion formulates multi-source sensors fusion based on factor graph, allowing a multitude of relative and absolute measurements which may be degraded, disturbed or even inaccessible. In the LIO-Fusion system, online initialization consists of point cloud feature extraction and matching, IMU preintegration, encoder integration, GNSS calibration and prior-map relocalization. Then, its global reinforcement module detects the reliability of GNSS/relocalization to obtain healthy GNSS/relocalization factors, whereas the local reinforcement module uses a sub-factor graph to fuse prior estimation results for the reinforced local odometry factor. Finally, the basic LiDAR/IMU factors, healthy GNSS/relocalization factors and reinforced local odometry factor are jointly used to constrain the system state in the main factor graph such that low-drift odometry under LiDAR degradation can be reliably obtained and corrected globally. We extensively evaluated the real-time LIO-Fusion system by real-world experiments, and compared its performance to other state-of-the-art methods on large-scale datasets collected in the urban and hazardous environments. Results have shown that LIO-Fusion yielded high precision localization and mapping accuracy as well as robustness to sensor failures.

Currently, the autonomous positioning of unmanned ground vehicles (UGVs) still faces the problems of insufficient persistence and poor reliability, especially in the challenging scenarios where satellites are denied, or the sensing modalities such as vision or laser are degraded. Based on multimodal information fusion and failure detection (FD), this article proposes a high-robustness and low-drift state estimation system suitable for multiple scenes, which integrates light detection and ranging (LiDAR), inertial measurement units (IMUs), stereo camera, encoders, attitude and heading reference system (AHRS) in a loose coupling way. Firstly, a state estimator with variable fusion mode is designed based on the error-state extended Kalman filtering (ES-EKF), which can fuse encoder-AHRS subsystem (EAS), visual-inertial subsystem (VIS), and LiDAR subsystem (LS) and change its integration structure online by selecting a fusion mode. Secondly, in order to improve the robustness of the whole system in challenging environments, an information manager is created, which judges the health status of subsystems by degeneration metrics, and then online selects appropriate information sources and variables to enter the estimator according to their health status. Finally, the proposed system is extensively evaluated using the datasets collected from six typical scenes: street, field, forest, forest-at-night, street-at-night and tunnel-at-night. The experimental results show our framework is better or comparable accuracy and robustness than existing publicly available systems.

In actual traffic scenarios, the environment is complex and constantly changing, with many vehicles that have substantial similarities, posing significant challenges to vehicle tracking research based on deep learning. To address these challenges, this article investigates the application of the DeepSORT (simple online and realtime tracking with a deep association metric) multitarget tracking algorithm in vehicle tracking. Due to the strong dependence of the DeepSORT algorithm on target detection, a YOLOv5s_DSC vehicle detection algorithm based on the YOLOv5s algorithm is proposed, which provides accurate and fast vehicle detection data to the DeepSORT algorithm. Compared to YOLOv5s, YOLOv5s_DSC has no more than a 1% difference in optimal mAP0.5 (mean average precision), precision rate, and recall rate, while reducing the number of parameters by 23.5%, the amount of computation by 32.3%, the size of the weight file by 20%, and increasing the average processing speed of each image by 18.8%. After integrating the DeepSORT algorithm, the processing speed of YOLOv5s_DSC + DeepSORT reaches up to 25 FPS, and the system exhibits better robustness to occlusion.

An image encryption and decryption method of fractional order chaotic systems (FOCS) with predefined time synchronization is proposed in this article. Compared with the existing integer order chaotic systems (IOCS), fractional order chaotic systems has the advantage of increasing the complexity of the ciphertext. At the same time, by using the predefined synchronization time, the key space is expanded, the complexity of the key is increased, and the security of the algorithm is improved. To further improve the security of encryption and decryption process, this article uses a combination of DNA encoding, row/column cyclic shift and XOR diffusion, position scrambling and Arnold scrambling. The simulation tests of image encryption and decryption are carried out, and the effectiveness and advantages of the proposed encryption/decryption method are verified by histogram analysis, correlation analysis, entropy analysis, key sensitivity analysis and plaintext sensitivity analysis.

In a long-endurance underwater vehicle rotational inertial navigation system (RINS), the rotation strategy significantly impacts navigation accuracy. To address the limitations of the dual-axis 16-position rotation modulation scheme, specifically, its large rotation angles and inability to modulate errors arising from the coupling of scale factors and Earth’s rotation rate. This article proposes a geocentric frame-based tri-axial 32-position rotation modulation method. This method suppresses navigation errors by altering the direction of the rotation axis and increasing the rotation sequence. It simultaneously isolates the Earth’s rotation rate, thereby eliminating the coupling error between inertial sensor scale factors and Earth rotation, and enhancing system navigation accuracy. Experimental results demonstrate that the system achieves a navigation positioning accuracy of approximately 2.2 nautical miles (n mile) over 72h. Compared to a single-axis RINS, positioning accuracy is improved by a factor of 8. Compared to a dual-axis RINS, positioning accuracy is enhanced by 0.75 n mile. These results validate the effectiveness of the proposed method in improving navigation accuracy.