BEVLCD+: Real-Time and Rotation-Invariant Loop Closure Detection Based on BEV of Point Cloud

Cao, Danyang; Yue, Haosong; Liu, Zhong; Wu, Xingming; Chen, Weihai

doi:10.1109/tim.2023.3315358

IEEE Trans. Instrum. Meas.

2023

DOI: 10.1109/tim.2023.3315358

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BEVLCD+: Real-Time and Rotation-Invariant Loop Closure Detection Based on BEV of Point Cloud

Danyang Cao,

Haosong Yue,

Zhong Liu

et al.

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2024

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Cited by 3 publications

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FELC-SLAM: feature extraction and loop closure optimized lidar SLAM system

Gao,

Li,

et al. 2024

Meas. Sci. Technol.

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Simultaneous Localization and Mapping (SLAM) is one of the key technologies in robot navigation and autonomous driving, playing an important role in robot navigation. Due to the sparsity of LiDAR data and the singularity of point cloud features, accuracy loss of LiDAR SLAM can occur during point cloud matching and localization. In response to these issues, this paper proposes a LiDAR Measurement SLAM algorithm that integrates multi type geometric feature extraction and optimized point cloud registration algorithms. This article first adopts advanced ground segmentation methods and feature segmentation strategies, including ground features, edge features, planar features, and spherical features, to improve matching accuracy. In addition, this article improves the previous method for extracting edge and planar features, extracting clearer and more robust line and surface features to address the degradation of geometric features. Finally, by introducing a robust decoupling global registration method for loop closure detection in the backend of the system, the sparsity problem of distant point clouds and the degradation problem caused by the reduction of inner layers in point cloud registration were effectively solved. In the evaluation of the KITTI dataset, our algorithm reduced absolute trajectory error values by 60%, 29%, and 71% compared to LeGO-LOAM in multi loop and feature constrained scenarios (such as sequences 00, 01, and 02), respectively. The evaluation of the M2DGR and Botanic Garden datasets also indicates that the positioning accuracy of our algorithm is superior to other advanced LiDAR SLAM algorithms.

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FELC-SLAM: feature extraction and loop closure optimized lidar SLAM system

Gao,

Li,

et al. 2024

Meas. Sci. Technol.

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Cross Fusion of Point Cloud and Learned Image for Loop Closure Detection

Yue,

Cao,

Liu

et al. 2024

IEEE Robot. Autom. Lett.

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Enhancing 3D Rock Localization in Mining Environments Using Bird’s-Eye View Images from the Time-of-Flight Blaze 101 Camera

Kern,

Rodriguez-Guillen,

Urrea

et al. 2024

Technologies

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The mining industry faces significant challenges in production costs, environmental protection, and worker safety, necessitating the development of autonomous systems. This study presents the design and implementation of a robust rock centroid localization system for mining robotic applications, particularly rock-breaking hammers. The system comprises three phases: assembly, data acquisition, and data processing. Environmental sensing was accomplished using a Basler Blaze 101 three-dimensional (3D) Time-of-Flight (ToF) camera. The data processing phase incorporated advanced algorithms, including Bird’s-Eye View (BEV) image conversion and You Only Look Once (YOLO) v8x-Seg instance segmentation. The system’s performance was evaluated using a comprehensive dataset of 627 point clouds, including samples from real mining environments. The system achieved efficient processing times of approximately 5 s. Segmentation accuracy was evaluated using the Intersection over Union (IoU), reaching 95.10%. Localization precision was measured by the Euclidean distance in the XY plane (EDXY), achieving 0.0128 m. The normalized error (enorm) on the X and Y axes did not exceed 2.3%. Additionally, the system demonstrated high reliability with R2 values close to 1 for the X and Y axes, and maintained performance under various lighting conditions and in the presence of suspended particles. The Mean Absolute Error (MAE) in the Z axis was 0.0333 m, addressing challenges in depth estimation. A sensitivity analysis was conducted to assess the model’s robustness, revealing consistent performance across brightness and contrast variations, with an IoU ranging from 92.88% to 96.10%, while showing greater sensitivity to rotations.

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BEVLCD+: Real-Time and Rotation-Invariant Loop Closure Detection Based on BEV of Point Cloud

Cited by 3 publications

References 44 publications

FELC-SLAM: feature extraction and loop closure optimized lidar SLAM system

FELC-SLAM: feature extraction and loop closure optimized lidar SLAM system

Cross Fusion of Point Cloud and Learned Image for Loop Closure Detection

Enhancing 3D Rock Localization in Mining Environments Using Bird’s-Eye View Images from the Time-of-Flight Blaze 101 Camera

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