关键词: slam, 点线特征, 多IMU融合, 自主导航, 图优化, 传感器融合, 激光雷达, 视觉传感器

Abstract: Accurate and efficient Simultaneous Localization and Mapping (SLAM) algorithms are crucial in mobile robotics and autonomous driving technologies. Existing SLAM methods often face challenges in precision and robustness when dealing with complex environments and dynamic scenes. This paper proposes a SLAM algorithm based on point-line features and multi-IMU (Inertial Measurement Unit) fusion to address these challenges. This study utilizes multi-sensor fusion technology, including LiDAR and cameras, to extract point and line features from the environment. These geometric features provide rich environmental information, aiding in the construction of more detailed and accurate maps. During the point-line feature extraction process, an optimization-based feature matching algorithm is employed to ensure the accuracy and stability of feature extraction. Multi-IMU fusion technology enhances the system's motion estimation capabilities. Multi-IMU fusion not only improves the robustness of a single IMU in high-dynamic environments but also, through optimized sensor data fusion algorithms, provides more precise pose estimation. The experimental section verifies the proposed SLAM algorithm in various typical indoor and outdoor environments, including static and dynamic scenes. Results indicate that the proposed SLAM algorithm significantly improves positioning accuracy, mapping quality, and real-time performance. Compared to traditional methods, this algorithm performs superiorly in complex environments, effectively handling environmental changes and noise disturbances.

Key words: slam, point-line feature, multi-IMU fusion, autonomous navigation, Graph Optimization, sensor fusion, Light Detection And Ranging （LiDAR）, vision sensor