Abstract: To address the issues of motion distortion and error accumulation in SLAM(Simultaneous Localization and Mapping) systems during prolonged operations, a mapping method called IRIS-LOAM ( Lidar-Iris Based Lidar Odometry and Mapping in Real-Time ) is proposed, which leverages Lidar-Iris to construct global descriptors for loop closure detection. This algorithm has two major  innovations based on the LOAM algorithm.First, in the data processing stage, it integrates lidar data with IMU(Inertial Measurement Unit) data and uses the IMU data to correct the point cloud data. Second, in the mapping optimization stage, it employs an information matrix-based graph optimization algorithm and utilizes Lidar-Iris global descriptors for loop closure detection of key frames. And it preprocesses the input point cloud to improve the time efficiency of optimization. Comparing the improved algorithm with A _ LOAM through experiments, the results show that IRIS-LOAM achieves better mapping performance in various real-world scenarios, demonstrating its feasibility and practicality.

Key words: simultaneous localization and mapping (SLAM), lidar, loop closure detection