Consistent ICP for the registration of sparse and inhomogeneous point clouds

Luong, Hiêp; Vlaminck, Michiel; Goeman, Werner; Philips, Wilfried

doi:10.1109/cce.2016.7562647

Cited by 9 publications

(4 citation statements)

References 21 publications

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“…The experimental results show that V-GICP reduces the computational load of the system while ensuring accuracy with the G-ICP algorithm. Nevertheless, the various G-ICP algorithms cannot meet the requirements for system real-time in real scenarios [20,21]. Therefore, the bottleneck of G-ICP algorithms and their variants is how to guarantee the matching accuracy and at the same time significantly reduce the computational load [22].…”

Section: Related Workmentioning

confidence: 99%

Low‐cost solid‐state LiDAR/inertial‐based localization with prior map for autonomous systems in urban scenarios

Zhong

Huang

Zhang

et al. 2022

IET Intelligent Trans Sys

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A low‐cost and accurate positioning solution is significant for the massive deployment of fully autonomous driving vehicles (ADV). Conventional mechanical LiDAR has proven its performance, but its high cost hinders the massive production of autonomous vehicles. This paper proposes a low‐cost LiDAR/inertial‐based localization solution for autonomous systems with prior maps in urban areas. Instead of relying on the costly mechanical LiDAR, this paper proposes to utilize the solid‐state LiDAR (SSL) with the prior map to estimate the position of the vehicle by matching the real‐time point clouds from the SSL and the prior map using the normal distribution transformation (NDT) algorithm. However, the field of view (FOV) of the SSL is signifcantly smaller than the conventional mechanical LiDAR, which can easily lead to failure during the NDT map matching. To fill this gap, this paper proposes to exploit the complementariness of the inertial measurement unit (IMU) and the SSL, where the IMU pre‐integration provides a coarse but high‐frequency initial guess to the map matching. To evaluate the effectiveness of the proposed method in this paper, the authors collect the dataset in two typical urban scenarios through a pedestrian hand‐hold and a vehicle driving condition. The results reveal that the SSL‐only‐based localization is significantly challenged in dynamic scenarios. With the help of the IMU, the robustness of the proposed method is significantly improved, achieving an accuracy of within 0.5 m. To show the sensitivity of the SSL‐based map matching against the initial guess of the state, this paper also presents the convergence results of the map matching under different levels of accuracy in terms of the initial guess.

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Section: Related Workmentioning

confidence: 99%

Low‐cost solid‐state LiDAR/inertial‐based localization with prior map for autonomous systems in urban scenarios

Zhong

Huang

Zhang

et al. 2022

IET Intelligent Trans Sys

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“…Similarly, based on this fixed distance, a specific threshold between a pair of points, correspondences can also be rejected with the worst n% of pairs [99], or with multiple of the standard deviation of distances [104]. However, rejection based on a fixed percentage or threshold usually is inflexible in dealing with incomplete point clouds [105]. erefore, other rejection strategies were introduced, such as median distance [106], dynamic threshold [99], RANSAC-based [107], normal compatibility [108], and duplicate matching [109] methods.…”

Section: Point Rejectionmentioning

confidence: 99%

A Tutorial Review on Point Cloud Registrations: Principle, Classification, Comparison, and Technology Challenges

Wang

Zhang

2021

Mathematical Problems in Engineering

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A point cloud as a collection of points is poised to bring about a revolution in acquiring and generating three-dimensional (3D) surface information of an object in 3D reconstruction, industrial inspection, and robotic manipulation. In this revolution, the most challenging but imperative process is point could registration, i.e., obtaining a spatial transformation that aligns and matches two point clouds acquired in two different coordinates. In this survey paper, we present the overview and basic principles, give systematical classification and comparison of various methods, and address existing technical problems in point cloud registration. This review attempts to serve as a tutorial to academic researchers and engineers outside this field and to promote discussion of a unified vision of point cloud registration. The goal is to help readers quickly get into the problems of their interests related to point could registration and to provide them with insights and guidance in finding out appropriate strategies and solutions.

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“…It is also worth mentioning that there a number of approaches in the literature that deal with registration of incomplete point clouds (also referred to as sparse point clouds). Some examples include SparseICP of Bouaziz et al (2013), Luong et al (2016), and Yuan et al (2018), who dealt with the registration of an incomplete point cloud obtained from sensors such as lidars. Bouaziz et al (2013) reformulated the objective function of ICP with a sparsity inducing norm.…”

Section: Related Workmentioning

confidence: 99%

“…Bouaziz et al (2013) reformulated the objective function of ICP with a sparsity inducing norm. Luong et al (2016) used a probabilistic data association similar to CPD. Yuan et al (2018) used an encoder–decoder network to complete the point cloud, using a network trained on similar point cloud datasets.…”

Section: Related Workmentioning

confidence: 99%

Registration with a small number of sparse measurements

Srivatsan

Zevallos

Vagdargi

et al. 2019

The International Journal of Robotics Research

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This work introduces a method for performing robust registration given the geometric model of an object and a small number (less than 20) of sparse point and surface normal measurements of the object’s surface. Such a method is of critical importance in applications such as probing-based surgical registration, contact-based localization, manipulating objects devoid of visual features, etc. Our approach for sparse point and normal registration (SPNR) is iterative in nature. In each iteration, the current best pose estimate is perturbed to generate several candidate poses. Among the generated poses, one pose is selected as the best, by evaluating an inexpensive cost function. This pose is used as the initial condition to estimate the locally optimum registration. This process is repeated until the registration estimate converges within a tolerance bound. Two variants are developed: deterministic (dSPNR) and probabilistic (pSPNR). The dSPNR is faster than pSPNR in converging to the local optimum, but the pSPNR requires fewer parameters to be tuned. The pSPNR also provides pose-uncertainty information in addition to the registration estimate. Both approaches were evaluated in simulation using various standard datasets and then compared with results obtained using state-of-the-art methods. Upon comparison with other methods, both dSPNR and pSPNR were found to be robust to initial pose errors as well as noise in measurements. The effectiveness of the approaches are also demonstrated with robot experiments for the application of probing-based registration.

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Consistent ICP for the registration of sparse and inhomogeneous point clouds

Cited by 9 publications

References 21 publications

Low‐cost solid‐state LiDAR/inertial‐based localization with prior map for autonomous systems in urban scenarios

Low‐cost solid‐state LiDAR/inertial‐based localization with prior map for autonomous systems in urban scenarios

A Tutorial Review on Point Cloud Registrations: Principle, Classification, Comparison, and Technology Challenges

Registration with a small number of sparse measurements

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