Mr. Shumin Liu Profile

Shumin Liu

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Proceedings Article | 5 July 2024 Paper

Lidar SLAM algorithm based on bag of words loop detection

Shumin Liu, Bingbing Wu, Xingfeng Chen, Jiannan Dan

Proceedings Volume 13184, 131843L (2024) https://doi.org/10.1117/12.3033104

KEYWORDS: Point clouds, LIDAR, Detection and tracking algorithms, Feature extraction, Pose estimation, Mathematical optimization, Visualization, Environmental sensing

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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.

Proceedings Article | 14 November 2007 Paper

Improved topographic correction approach for radiation of remote sensing image

Shumin Liu, Xingfa Gu, Hongyou Liang, Feiming Wei, Yang Dong

Proceedings Volume 6790, 67902O (2007) https://doi.org/10.1117/12.750971

KEYWORDS: Remote sensing, Vegetation, Statistical analysis, Sun, Reflectivity, Atmospheric corrections, Earth observing sensors, Satellite imaging, Satellites, Landsat

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Remote sensing images of rugged areas are severely affected by the topographic effects. Usually, the effects can cause plenty of shades in the images and result in a high variation in the reflectance response for similar vegetation types. Accordingly, these effects will strongly affect the quality of vegetation classification. In general, the irradiance which a slope accepts contains three parts: the direct solar irradiance, the diffuse sky irradiance and the irradiance from adjacent terrain. But the area facing away from the sun cannot accept the direct solar irradiance. The C correction method which takes into accounts the effects of the atmosphere and adjacent terrain has become a popular method for topographic correction. But the C corrected image has a problem of overcorrection, especially in the area of high incidence angles, since this method is based on the empirical linear correlation between observed radiance and the cosine values of solar incidence angles. In this paper, an improved C correction method is proposed to reduce this error and improve the accuracy of classification.

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