VODRAC: Efficient and robust correspondence-based point cloud registration with extreme outlier ratios

Hu, Enwen; Sun, Lei

doi:10.1016/j.jksuci.2022.05.021

Cited by 5 publications

(2 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The new technique, called RANSIC, shows improved performance in highoutlier regimes but will still struggle when no unique model of the data exists. A technique called VODRAC introduced by Hu and Sun (2023) also improves on the RANSAC and RANSIC algorithms by using a two-point sampling strategy combined with a weight-based voting strategy that speeds up the consensus maximization and is robust in 99% outlier regimes.…”

Section: Related Workmentioning

confidence: 99%

Group-$k$ Consistent Measurement Set Maximization for Robust Outlier Detection

Forsgren

Vasudevan

Kaess

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

This paper unifies the theory of consistent-set maximization for robust outlier detection in a simultaneous localization and mapping framework. We first describe the notion of pairwise consistency before discussing how a consistency graph can be formed by evaluating pairs of measurements for consistency. Finding the largest set of consistent measurements is transformed into an instance of the maximum clique problem and can be solved relatively quickly using existing maximum-clique solvers. We then generalize our algorithm to check consistency on a group-k basis by using a generalized notion of consistency and using generalized graphs. We also present modified maximum clique algorithms that function on generalized graphs to find the set of measurements that is internally group-k consistent. We address the exponential nature of group-k consistency and present methods that can substantially decrease the number of necessary checks performed when evaluating consistency. We extend our prior work to multi-agent systems in both simulation and hardware and provide a comparison with other state-of-the-art methods.

show abstract

Section: Related Workmentioning

confidence: 99%

Group-$k$ Consistent Measurement Set Maximization for Robust Outlier Detection

Forsgren

Vasudevan

Kaess

et al. 2022

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

View full text Add to dashboard Cite

show abstract

“…Our approach follows the correspondence-based methodology, typically involving two primary stages [15]. First, obtain point correspondences.…”

Section: Introductionmentioning

confidence: 99%

A Robust Multi-Local to Global with Outlier Filtering for Point Cloud Registration

Chen,

Mei,

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

As a prerequisite for many 3D visualization tasks, point cloud registration has a wide range of applications in 3D scene reconstruction, pose estimation, navigation, and remote sensing. However, due to the limited overlap of point clouds, the presence of noise and the incompleteness of the data, existing feature-based matching methods tend to produce higher outlier matches, thus reducing the quality of the registration. Therefore, the generation of reliable feature descriptors and the filtering of outliers become the key to solving these problems. To this end, we propose a multi-local-to-global registration (MLGR) method. First, in order to obtain reliable correspondences, we design a simple but effective network module named the local geometric network (LG-Net), which can generate discriminative feature descriptors to reduce the outlier matches by learning the local latent geometric information of the point cloud. In addition, we propose a multi-local-to-global registration strategy to further filter outlier matches. We compute the hypothetical transformation matrix from local patch matches. The point match evaluated as an inlier under multiple hypothetical transformations will receive a higher score, and low-scoring point matches will be rejected. Finally, our method is quite robust under different numbers of samples, as it does not require sampling a large number of correspondences to boost the performance. The numerous experiments on well-known public datasets, including KITTI, 3DMatch, and ModelNet, have proven the effectiveness and robustness of our method. Compared with the state of the art, our method has the lowest relative rotation error and relative translation error on the KITTI, and consistently leads in feature matching recall, inlier ratio, and registration recall on 3DMatch under different numbers of point correspondences, which proves the robustness of our method. In particular, the inlier ratio is significantly improved by 3.62% and 4.36% on 3DMatch and 3DLoMatch, respectively. In general, the performance of our method is more superior and robust than the current state of the art.

show abstract