## 3 Multi-Sensor-Based Remote Autonomous Navigation and Close-Range  
### Remote Autonomous Navigation Based on LiDAR, Vision, and IMU Fusion  
In robotic applications, visual SLAM relies on the quality of environmental features and is easily affected by lighting conditions, which can lead to poor performance in low-texture or dark environments. When a robot moves in a stable straight line, the average angular velocity and motion acceleration measured by the IMU are zero, and relying solely on vision can cause system scale drift. Different terrains can cause varying levels of vibration noise in the IMU, impacting algorithm performance and potentially leading to system divergence. Additionally, in large-scale environments, the ratio of the motion baseline to feature depth is very small, which makes feature depth estimation inaccurate and causes the visual constraints to degrade. While LiDAR SLAM offers better accuracy and robustness compared to visual SLAM, the laser point clouds are relatively sparse, with lower resolution at longer distances, and the data association is not as effective as in visual SLAM, particularly in long corridors and open environments. The IMU can provide short-term state estimation through pre-integration, but noise and zero bias cause the integral values to drift quickly. Encoders can measure one-dimensional motion distance but rely heavily on the environment for position estimation, and significant errors can occur during wheel slip. To address these issues, this paper is based on the RTAB-Map [18, 19] system framework and combines the strengths of vision, LiDAR, and IMU to enhance system performance and robustness. The RTAB-Map is an open-source library for real-time localization and mapping based on appearance features. It achieves loop closure detection through a unique memory management approach, making it suitable for long-term and large-scale real-time online SLAM requirements [10, 20]. The generated maps can be used for complex tasks such as robot navigation and obstacle avoidance. This technology processes data in three parts: short-term memory (STM), working memory (WM), and long-term memory (LTM). The system framework is illustrated in Figure 3. The RTAB-Map uses a graph structure to organize the map, consisting of nodes and the edges connecting them. Sensor data, once synchronized, are stored in the short-term memory module. The short-term memory (STM) module creates a node to remember odometry poses, raw sensor data, and additional information useful for the next module. Figure 3: RTAB-Map system framework. When STM creates a new node, it can use depth images, laser scan data, and point cloud data to generate the corresponding local map. If a three-dimensional map is selected, it can be created directly from the three-dimensional point cloud or processed through a three-dimensional beam model to create a three-dimensional local map. An independent ROS node provides the odometry information required by the RTAB-Map. Due to the cumulative errors in odometry used for local mapping, loop detection and global optimization are necessary. In the RTAB-Map, loop detection includes visual loop closure detection and laser similarity detection, while global optimization uses pose graph optimization methods. Visual loop closure detection is based on the visual bag-of-words model and Bayesian filters. The visual bag-of-words model quickly calculates the similarity between the current pose node and candidate loop closure nodes. It achieves efficient image matching and similarity computation by extracting image features and constructing bag-of-words representations. The Bayesian filter maintains the probability distribution of candidate loop closure node similarities and updates the probability distribution to select the most likely loop closure node. Visual loop closure detection effectively identifies similar scenes at different times and locations, thus addressing the problem of cumulative odometry errors.