A LiDAR SLAM With PCA-Based Feature Extraction and Two-Stage Matching

Guo, Shiyi; Zheng, Rong; Wang, Shuo; Wu, Yihong

doi:10.1109/tim.2022.3156982

Cited by 21 publications

(4 citation statements)

References 29 publications

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“… The edge and planar features in industrial park environments exhibit relative prominence; however, they are often accompanied by numerous unstable features. Traditional methods encounter challenges in efficiently and accurately extracting high-quality feature points, as well as in concurrently extracting ground points in a targeted manner [ 25 , 26 , 27 , 28 ]. …”

Section: Introductionmentioning

confidence: 99%

Robust Localization of Industrial Park UGV and Prior Map Maintenance

Luo

Liu

Zou³

et al. 2023

Sensors

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The precise localization of unmanned ground vehicles (UGVs) in industrial parks without prior GPS measurements presents a significant challenge. Simultaneous localization and mapping (SLAM) techniques can address this challenge by capturing environmental features, using sensors for real-time UGV localization. In order to increase the real-time localization accuracy and efficiency of UGVs, and to improve the robustness of UGVs’ odometry within industrial parks—thereby addressing issues related to UGVs’ motion control discontinuity and odometry drift—this paper proposes a tightly coupled LiDAR-IMU odometry method based on FAST-LIO2, integrating ground constraints and a novel feature extraction method. Additionally, a novel maintenance method of prior maps is proposed. The front-end module acquires the prior pose of the UGV by combining the detection and correction of relocation with point cloud registration. Then, the proposed maintenance method of prior maps is used to hierarchically and partitionally segregate and perform the real-time maintenance of the prior maps. At the back-end, real-time localization is achieved by the proposed tightly coupled LiDAR-IMU odometry that incorporates ground constraints. Furthermore, a feature extraction method based on the bidirectional-projection plane slope difference filter is proposed, enabling efficient and accurate point cloud feature extraction for edge, planar and ground points. Finally, the proposed method is evaluated, using self-collected datasets from industrial parks and the KITTI dataset. Our experimental results demonstrate that, compared to FAST-LIO2 and FAST-LIO2 with the curvature feature extraction method, the proposed method improved the odometry accuracy by 30.19% and 48.24% on the KITTI dataset. The efficiency of odometry was improved by 56.72% and 40.06%. When leveraging prior maps, the UGV achieved centimeter-level localization accuracy. The localization accuracy of the proposed method was improved by 46.367% compared to FAST-LIO2 on self-collected datasets, and the located efficiency was improved by 32.33%. The z-axis-located accuracy of the proposed method reached millimeter-level accuracy. The proposed prior map maintenance method reduced RAM usage by 64% compared to traditional methods.

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Section: Introductionmentioning

confidence: 99%

Robust Localization of Industrial Park UGV and Prior Map Maintenance

Luo

Liu

Zou³

et al. 2023

Sensors

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“…The 3D visualization of a complete building structure based on the TomoSAR point cloud can not only rely on the TomoSAR point cloud generated by the SAR image of rising and falling tracks but also be realized by combining TomoSAR and LiDAR point clouds. The backpack mobile 3D laser scanner uses the laser SLAM principle, and the operation is very simple [ 7 , 8 ]. It restores the spatial 3D data through the algorithm as a function of its attitude data and laser point cloud.…”

Section: Introductionmentioning

confidence: 99%

Automatic Registration of Homogeneous and Cross-Source TomoSAR Point Clouds in Urban Areas

Pang

Liu

et al. 2023

Sensors

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Building reconstruction using high-resolution satellite-based synthetic SAR tomography (TomoSAR) is of great importance in urban planning and city modeling applications. However, since the imaging mode of SAR is side-by-side, the TomoSAR point cloud of a single orbit cannot achieve a complete observation of buildings. It is difficult for existing methods to extract the same features, as well as to use the overlap rate to achieve the alignment of the homologous TomoSAR point cloud and the cross-source TomoSAR point cloud. Therefore, this paper proposes a robust alignment method for TomoSAR point clouds in urban areas. First, noise points and outlier points are filtered by statistical filtering, and density of projection point (DoPP)-based projection is used to extract TomoSAR building point clouds and obtain the facade points for subsequent calculations based on density clustering. Subsequently, coarse alignment of source and target point clouds was performed using principal component analysis (PCA). Lastly, the rotation and translation coefficients were calculated using the angle of the normal vector of the opposite facade of the building and the distance of the outer end of the facade projection. The experimental results verify the feasibility and robustness of the proposed method. For the homologous TomoSAR point cloud, the experimental results show that the average rotation error of the proposed method was less than 0.1°, and the average translation error was less than 0.25 m. The alignment accuracy of the cross-source TomoSAR point cloud was evaluated for the defined angle and distance, whose values were less than 0.2° and 0.25 m.

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“…A suitable similarity distance calculation method can achieve better accuracy and precision in the RUL prediction, therefore, the selection of similarity distance is a very important process for RUL prediction. However, in existing articles that employ similarity methods to calculate RUL, most studies use Euclidean distance [30][31][32] and cosine distance [33,34] for similarity calculation, leaving a noticeable gap in the exploration of other distance formulas. This article demonstrates the feasibility of using the Manhattan distance in similarity method prediction, providing new ideas for RUL prediction.…”

Section: Introductionmentioning

confidence: 99%

Remaining useful life prediction based on a PCA and similarity methods

Duan,

Shen,

Guo

et al. 2023

Meas. Sci. Technol.

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Aircraft engine failures or damages not only incur substantial financial losses but also present risks of injuries or even fatalities. Hence, it is of utmost importance to devise an effective method to predict potential failures in advance, thereby mitigating accidents and minimizing losses. This paper proposes a novel approach that combines principal component analysis (PCA) with similarity methods to establish a degradation trajectory database and predict the remaining useful life (RUL) of new engines by identifying similar trajectories. Firstly, the data dimensionality is reduced using PCA to create a health index. The validity of the reduced data is confirmed by calculating the Spearman correlation coefficient between the index and the RUL. During the similarity comparison process, the Manhattan distance is employed as the calculation method, and parameter optimization is performed on the length of selected time segments and the number of chosen similar trajectories to optimize the similarity RUL prediction model, resulting in the best prediction results among all engine test sets. Notably, this paper introduces the feasibility of employing the Manhattan distance in similarity method prediction, which diverges from the prevalent use of Euclidean distance in the current literature. This finding offers innovative ideas and perspectives for advancing RUL prediction methodologies. By adopting the proposed approach, the occurrence of accidents and losses associated with aircraft engine failures can be substantially reduced, leading to enhanced safety and economic benefits.

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A LiDAR SLAM With PCA-Based Feature Extraction and Two-Stage Matching

Cited by 21 publications

References 29 publications

Robust Localization of Industrial Park UGV and Prior Map Maintenance

Robust Localization of Industrial Park UGV and Prior Map Maintenance

Automatic Registration of Homogeneous and Cross-Source TomoSAR Point Clouds in Urban Areas

Remaining useful life prediction based on a PCA and similarity methods

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