Point Set Registration for 3D Range Scans Using Fuzzy Cluster-Based Metric and Efficient Global Optimization

Liao, Qianfang; Sun, Da; Andreasson, Henrik

doi:10.1109/tpami.2020.2978477

Cited by 23 publications

(61 citation statements)

References 48 publications

(95 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…No additional parameters to NDT are required. f) FuzzyQA: FuzzyQA [11] measures the alignment quality by a ratio ρ = AFCCD AFPCD , where AFCCD and AFPCD are two indexes describing the points' disposition and dispersion around fuzzy cluster centers. The two point clouds are coarsely aligned if ρ < 1.…”

Section: A Evaluated Methodsmentioning

confidence: 99%

See 1 more Smart Citation

CorAl -- Are the point clouds Correctly Aligned?

Adolfsson¹,

Magnusson²,

Liao³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and without environment-specific parameters. We propose "CorAl", an alignment quality measure and alignment classifier for point cloud pairs, which facilitates the ability to introspectively assess the performance of registration. CorAl compares the joint and the separate entropy of the two point clouds.The separate entropy provides a measure of the entropy that can be expected to be inherent to the environment. The joint entropy should therefore not be substantially higher if the point clouds are properly aligned. Computing the expected entropy makes the method sensitive also to small alignment errors, which are particularly hard to detect, and applicable in a range of different environments. We found that CorAl is able to detect small alignment errors in previously unseen environments with an accuracy of 95% and achieve a substantial improvement to previous methods.

show abstract

Section: A Evaluated Methodsmentioning

confidence: 99%

“…Liao et al [11] recently proposed a registration method based on fuzzy clusters, which involves a registration quality assessment. This fuzzy cluster-based quality assessment (FuzzyQA) compares the similarity of dispersion and disposition of points around fuzzy cluster centers.…”

Section: Related Workmentioning

confidence: 99%

CorAl -- Are the point clouds Correctly Aligned?

Adolfsson¹,

Magnusson²,

Liao³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another common alternative is the point-to-line distance [9], [10] or -as a generalization -a point-todistribution [11], [20] or distribution-to-distribution [13] measure; where local surface descriptors are computed using the spatial distribution of points within a neighborhood. In a similar vein, Liao et al [14] propose distribution-todistribution registration based on fuzzy clusters, and estimate coarse alignment quality via the dispersion and disposition of points around fuzzy cluster centers. These methods have also been shown to generalize poorly for assessing 3D lidar scan alignment in different environments [17].…”

Section: A Cost Functions For Scan Registrationmentioning

confidence: 99%

“…A particular benefit of the CorAl method is that it generalizes well, so that if it has been trained in one environment, the same parameters can be used in other, unseen, environments. Some examples of methods that have been used in practice to assess the alignment quality include point-to-point or point-to-plane distances [9], [10], point-to-distribution [11], [12] or distribution-to-distribution [13], [14] likelihood estimates, mean map entropy [15] or dense radar-image comparison [16]. However, except for some notable exceptions [12], [17], few studies in the literature have specifically and methodically targeted the measurement of alignment correctness.…”

Section: Introductionmentioning

confidence: 99%

CorAl: Introspection for Robust Radar and Lidar Perception in Diverse Environments Using Differential Entropy

Adolfsson,

Castellano-Quero,

Magnusson

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and pre-processing, as well as failure awareness through introspection, for example the abaility to self-assess localization performance. This paper presents CorAl: a principled, intuitive, and generalizable method to measure the quality of alignment between pairs of point clouds, which learns to detect alignment errors in a selfsupervised manner. CorAl compares the differential entropy in the point clouds separately with the entropy in their union to account for entropy inherent to the scene. By making use of dual entropy measurements, we obtain a quality metric that is highly sensitive to small alignment errors and still generalizes well to unseen environments. In this work, we extend our previous work on lidar-only CorAl to radar data by proposing a two-step filtering technique that produces highquality point clouds from noisy radar scans. Thus we target robust perception in two ways: by introducing a method that introspectively assesses alignment quality, and applying it to an inherently robust sensor modality. We show that our filtering technique combined with CorAl can be applied to the problem of alignment classification, and that it detects small alignment errors in urban settings with up to 98% accuracy, and with up to 96% if trained only in a different environment. Our lidar and radar experiments demonstrate that CorAl outperforms previous methods both on the ETH lidar benchmark, which includes several indoor and outdoor environments, and the large-scale Oxford and MulRan radar data sets for urban traffic scenarios The results also demonstrate that CorAl generalizes very well across substantially different environments without the need of retraining.

show abstract

“…W E are entering a new era of human-robot shared work, where robots aid in surpassing human physical limitations and providing necessary assistance. Completely autonomous robot systems usually require good sensory mechanism for goal identification [1], [2], long training process [3], [4], and high-level dexterity [5], [6], which usually have limited performance in cluttered environment. Teleoperation in combination with human intelligence can, on the other hand, deliver a safe, reliable and robust performance.…”

Section: Introduction a Backgroundmentioning

confidence: 99%