Prof. Xingfeng Chen Profile

Prof. Xingfeng Chen

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Proceedings Article | 7 August 2024 Paper

Spectral simplification of PCA inter-class distance evaluation: a case of artificial breeding and wild Cordyceps sinensis identification

Kaiwen Zhou, Shumin Liu, Hejuan Du, Jiaguo Li, Dong Wang, Tingting Shi, Xingfeng Chen

Proceedings Volume 13229, 132291H (2024) https://doi.org/10.1117/12.3037998

KEYWORDS: Principal component analysis, Mahalanobis distance, Reflectivity, Spectroscopy, Equipment, Hyperspectral imaging, Covariance matrices, Short wave infrared radiation, Infrared radiation, Eigenvectors

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In recent years, with the increasing market demand for Cordyceps sinensis, the identification of artificial breeding varieties and wild varieties has become particularly important. The traditional identification method of Cordyceps sinensis is time-consuming and inefficient. Based on the advantages of hyperspectral detection technology, a simplified spectral method based on PCA inter-class distance evaluation was proposed. By performing PCA dimensionality reduction on the hyperspectral data of Cordyceps sinensis samples under different spectra, extracting principal components, calculating inter-class distance, and selecting the most recognizable spectra, the identification process was simplified. The preliminary results showed that the inter-class distance between artificial breeding and wild Cordyceps sinensis was the largest when SWIR-1 (1000-1800nm) spectrum was used only. When the Mahalanobis distance is used as an evaluation index, when only SWIR-1 spectrum is used, the Mahalanobis distance value reaches 5.21, which is larger than that of other spectra only. By simplifying the spectrum, the identification of artificial breeding and wild Cordyceps sinensis using only SWIR- 1 spectrum can be effective, and the hardware cost of the spectrometer can be reduced.

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.

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