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LOAM stands as a quintessential 3D Lidar SLAM algorithm capable of real-time robot positioning and mapping; however, it may succumb to positioning drift and mapping inaccuracies during prolonged operation. Addressing LOAM's limitations, LeGO-LOAM enhances robustness by integrating loop closure detection via ICP, yet it remains susceptible to false positives or omissions in expansive environments. In light of these challenges, this study introduces a Lidar SLAM algorithm that leverages a bag-of-words approach for loop closure detection. Adopting LOAM as the preliminary odometry, the method incorporates LinK3D alongside a bag-of-words model to devise a novel loop detection module. The process unfolds as follows: initially, the LinK3D algorithm is employed to extract and characterize point cloud features; subsequently, a hash data structure is utilized to construct the bag-of-words model for these descriptors. Thereafter, drawing inspiration from TF-IDF, the method expedites loop closure detection by computing the 6-DoF pose transformation between valid loop frames and the current frame. Ultimately, pose adjustments are refined using the graph optimization tool GTSAM. To enhance the feature representation and robustness of the LinK3D algorithm, this paper introduces an augmented LinK3D feature extraction technique, which integrates plane feature data. The algorithm's efficacy was ascertained through a series of tests on six Lidar point cloud sequences from the KITTI public dataset, including sequences 00, 05, and 09, benchmarked against classical SLAM algorithms such as A-LOAM and LeGO-LOAM. Evaluation across two dimensions—pose precision and mapping quality—confirmed the proposed algorithm's significant reduction in cumulative errors, elevated positioning accuracy, and enhanced mapping fidelity, all while meeting the real-time operational criteria.
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