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To address the issues of low localization and mapping accuracy, as well as map ghosting and drift, in indoor degraded environments using light detection and ranging-simultaneous localization and mapping (LiDAR SLAM), a real-time localization and mapping system integrating filtering and graph optimization theory is proposed. By incorporating filtering algorithms, the system effectively reduces localization errors and environmental noise. In addition, leveraging graph optimization theory, it optimizes the poses and positions throughout the SLAM process, further enhancing map accuracy and consistency. The purpose of this study resolves common problems such as map ghosting and drift, thereby achieving more precise real-time localization and mapping results.

The system consists of three main components: point cloud data preprocessing, tightly coupled inertial odometry based on filtering and backend pose graph optimization. First, point cloud data preprocessing uses the random sample consensus algorithm to segment the ground and extract ground model parameters, which are then used to construct ground constraint factors in backend optimization. Second, the frontend tightly coupled inertial odometry uses iterative error-state Kalman filtering, where the LiDAR odometry serves as observations and the inertial measurement unit preintegration results as predictions. By constructing a joint function, filtering fusion yields a more accurate LiDAR-inertial odometry. Finally, the backend incorporates graph optimization theory, introducing loop closure factors, ground constraint factors and odometry factors from frame-to-frame matching as constraints. This forms a factor graph that optimizes the map’s poses. The loop closure factor uses an improved scan-text-based loop closure detection algorithm for position recognition, reducing the rate of environmental misidentification.

A SLAM system integrating filtering and graph optimization technique has been proposed, demonstrating improvements of 35.3%, 37.6% and 40.8% in localization and mapping accuracy compared to ALOAM, lightweight and ground optimized lidar odometry and mapping and LiDAR inertial odometry via smoothing and mapping, respectively. The system exhibits enhanced robustness in challenging environments.

This study introduces a frontend laser-inertial odometry tightly coupled filtering method and a backend graph optimization method improved by loop closure detection. This approach demonstrates superior robustness in indoor localization and mapping accuracy.

This paper aims to synthesize a modular deployable truss antenna with the lower degree of freedom (DOF) and larger folding ratio. Because of the advantages of this kind of new truss antenna, the modules that make up the antenna can be deployed together by the synchronous motor drivers instead of twist springs to realize the controllable deployment.

The closed-loop branch equivalence method is proposed to synthesize the single DOF module and the large deployable reflector. The complex mechanism can be equivalently replaced by a simpler mechanism based on screw theory. The motion pairs are synthesized and optimized to make the curved surface achieve to the maximum folding ratio when the modular parabolic truss antenna is folded.

The results show that the 3(3RR-3RRR)-3RRR-3RRR planar module is a single DOF mechanism. Additionally, the adjacent parts of every two modules are connected with universal joints to obtain the new truss antenna when the modules are networked.

The configuration of this new modular deployable truss antenna can be synthesized to design the structure, and the proposed method can be applied to other space multi-loop coupling mechanism and other spacecraft.

This paper presents an approach to synthesizing the motion pairs, as well as the DOF analysis. The results lay a foundation for the further analysis of the deployable control and dynamics of this kind of antenna. And the new modular truss antenna has a practical application in aerospace engineering.