T-LOAM: Truncated Least Squares LiDAR-Only Odometry and Mapping in Real Time

Zhou, Pengwei; Guo, Xuexun; Pei, Xiaofei; Chen, Ci

doi:10.1109/tgrs.2021.3083606

Cited by 26 publications

(9 citation statements)

References 57 publications

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“…However, there are a large number of outliers in the set of correspondences which are obtained by feature descriptor matching. Given a correspondence set H = {(p i , q i )} N 1 , the correspondence-based 6-DOF point cloud registration can be formulated as a Truncated Least Squares (TLS) problem [34] considering the existence of noise and outliers: where R ∈ SO( 3) is an orthogonal matrix, t is a 3 × 1 translation vector, δ i is a noise bound, and c 2 is a proportional coefficient which can dispose of potential outliers in a rigorous or more tolerant way [41] and it is usually set to 1 [34]. And for a correspondence:…”

Section: Problem Formulationmentioning

confidence: 99%

“…To evaluate the effectiveness and robustness of the algorithm, we add N out = (50%, 60%, 70%, 80%, 90%, 95%, 96%, 97%, 98%, 99%) for testing, and each test is repeated 100 times. Some baseline as well as state-of-theart outlier removal methods are selected for comparison, they are RANSAC [16], FGR [33], Gore [37], Teaser++ [40], and clipper [41] respectively. See TABLE 1 for the setting of algorithm parameters.…”

Section: Simulationsmentioning

confidence: 99%

See 1 more Smart Citation

A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration

Li¹,

Wei²,

Xie³

et al. 2022

Preprint

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Registration is a basic yet crucial task in point cloud processing. In correspondence-based point cloud registration, matching correspondences by point feature techniques may lead to an extremely high outlier ratio. Current methods still suffer from low efficiency, accuracy, and recall rate. We use a simple and intuitive method to describe the 6-DOF (degree of freedom) curtailment process in point cloud registration and propose an outlier removal strategy based on the reliability of the correspondence graph. The method constructs the corresponding graph according to the given correspondences and designs the concept of the reliability degree of the graph node for optimal candidate selection and the reliability degree of the graph edge to obtain the global maximum consensus set. The presented method could achieve fast and accurate outliers removal along with gradual aligning parameters estimation. Extensive experiments on simulations and challenging real-world datasets demonstrate that the proposed method can still perform effective point cloud registration even the correspondence outlier ratio is over 99%, and the efficiency is better than the state-of-the-art.Code is available at https://github.com/WPC-WHU/GROR.

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Section: Problem Formulationmentioning

confidence: 99%

Section: Simulationsmentioning

confidence: 99%

A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration

Li¹,

Wei²,

Xie³

et al. 2022

Preprint

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show abstract

“…After completing interframe matching, LiDAR motion compensation is required. Mainstream laser SLAM Wireless Communications and Mobile Computing algorithms [20][21][22] unify the point cloud within a sweep to a timestamp, and this unified time point is often the start time of the scan. Then, using the result of the last interframe laser odometer as the motion between the current two frames and assuming that the current frame is also moving at a uniform speed, we can also estimate the position and pose of each point relative to the starting time.…”

Section: Related Workmentioning

confidence: 99%

High-Precision Motion Compensation for LiDAR Based on LiDAR Odometry

Qin

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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LiDAR plays a pivotal role in the field of unmanned driving, but in actual use, it is often accompanied by errors caused by point cloud distortion, which affects the accuracy of various downstream tasks. In this paper, we first describe the feature of point cloud and propose a new feature point selection method Soft-NMS-Select; this method can obtain uniform feature point distribution and effectively improve the result of subsequent point cloud registration. Then, the point cloud registration is completed through the screened feature points, and the odometry information is obtained. For the motion distortion generated in a sweep, the prior information of the LiDAR’s own motion is obtained by using two linear interpolations, thereby improving the effect of motion compensation. Finally, for the distortion caused by the motion of objects in the scene, Euclidean clustering is used to obtain the position and normal vector of the center point of the point cloud cluster, and the motion pose of the object is calculated according to the offset between adjacent sweeps and eliminated distortion. Essentially, our method is a general point cloud compensation method that is applicable to all uses of LiDAR. This paper inserts this method into three SLAM algorithms to illustrate the effectiveness of the method proposed in this paper. The experimental results show that this method can significantly reduce the APE of the original SLAM algorithm and improve the mapping result.

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“…With the development of the ranging sensor, the lidar slam algorithms become an widely-studied topic in the research area (Zhang and Singh, 2014, Zhang and Singh, 2017, Deschaud, 2018, Zhou et al, 2021. The most important work in the whole topic is the LOAM Singh, 2014, Zhang andSingh, 2017), which uses the curvature features to estimate the odometry of each scan frame.…”

Section: Lidar-slammentioning

confidence: 99%

A 3d Lidar Reconstruction Approach for Vegetation Detection in Power Transmission Networks

Zhou

Wen

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Vegetation management is important to the power transmission and distribution networks. The encompassed towering tree is always the key factor of the high impedance faults(HIFs).LiDAR is an efficient way to detect trees with 3D point cloud. The classical tree detection algorithm can handle the tree with high and distinct trunk,but limited to the tree with messy trunks. While the deeplearning based detection algorithms are also suffered from the terrain noise points. In this paper, we propose an efficient LiDAR reconstruction system which can efficiently reconstruct the point cloud of surrounding vegetation without the ground plane noise. We also use different weight strategies to improve the localization accuracy. We have conducted our system on the real power network environment and the height detection result shows that our algorithm has a better accuracy and robustness compared with the classical methods.

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T-LOAM: Truncated Least Squares LiDAR-Only Odometry and Mapping in Real Time

Cited by 26 publications

References 57 publications

A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration

A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration

High-Precision Motion Compensation for LiDAR Based on LiDAR Odometry

A 3d Lidar Reconstruction Approach for Vegetation Detection in Power Transmission Networks

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