Rotational Outlier Identification in Pose Graphs using Dual Decomposition

Karimian, Arman; Yang, Ziqi; Tron, Roberto

doi:10.1007/978-3-030-58577-8_24

Cited by 4 publications

(2 citation statements)

References 46 publications

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“…Pose averaging is a second fundamental state estimation problem, often encountered in camera-based applications such as structure from motion (SfM) [23]. A pose averaging study was performed investigating the effectiveness of the RLFs in a six degree-of-freedom problem with high outlier rates.…”

Section: Pose Averagingmentioning

confidence: 99%

Mind the Gap: Norm-Aware Adaptive Robust Loss for Multivariate Least-Squares Problems

Thomas

Forbes

2022

IEEE Robot. Autom. Lett.

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Measurement outliers are unavoidable when solving real-world robot state estimation problems. A large family of robust loss functions (RLFs) exists to mitigate the effects of outliers, including newly developed adaptive methods that do not require parameter tuning. All of these methods assume that residuals follow a zero-mean Gaussian-like distribution. However, in multivariate problems the residual is often defined as a norm, and norms follow a Chi-like distribution with a non-zero mode value. This produces a "mode gap" that impacts the convergence rate and accuracy of existing RLFs. The proposed approach, "Adaptive MB," accounts for this gap by first estimating the mode of the residuals using an adaptive Chi-like distribution. Applying an existing adaptive weighting scheme only to residuals greater than the mode leads to more robust performance and faster convergence times in two fundamental state estimation problems, point cloud alignment and pose averaging.

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Section: Pose Averagingmentioning

confidence: 99%

Mind the Gap: Norm-Aware Adaptive Robust Loss for Multivariate Least-Squares Problems

Thomas

Forbes

2022

IEEE Robot. Autom. Lett.

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“…Algorithms such as 'Principal Component Analysis'(PCA) ,'DeepSVDD' and 'Rotation based Outlier Detection'(ROD) were also looked at to tabulate the level of accuracy in predicting the outliers for the synthetically generated dataset. To name a few PCA [16], [17], ROD [18], [19], Local Outlier Factor [20], [21], DeepSVDD [22], [23] and more were used. In spite of the distinct strategies given out by various models, the unique methods proposed as a part of this research stood out in the following metrics Accuracy, Recall, Precision, Sensitivity and F1 score.…”

Section: Introductionmentioning

confidence: 99%

PAACDA: Comprehensive Data Corruption Detection Algorithm

et al. 2023

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With the advent of technology, data and its analysis are no longer just values and attributes strewn across spreadsheets, they are now seen as a stepping stone to bring about revolution in any significant field. Data corruption can be brought about by a variety of unethical and illegal sources, making it crucial to develop a method that is highly effective to identify and appropriately highlight the various corrupted data existing in the dataset. Detection of corrupted data, as well as recovering data from a corrupted dataset, is a challenging problem. This requires utmost importance and if not addressed at earlier stages may pose problems in later stages of data processing with machine or deep learning algorithms. In the following work we begin by introducing the PAACDA: Proximity based Adamic Adar Corruption Detection Algorithm and consolidating the results whilst particularly accentuating the detection of corrupted data rather than outliers. Current state of the art models, such as Isolation forest, DBSCAN also called "Density-Based Spatial Clustering of Applications with Noise" and others, are reliant on fine-tuning parameters to provide high accuracy and recall, but they also have a significant level of uncertainty when factoring the corrupted data. In the present work, the authors look into the most niche performance issues of several unsupervised learning algorithms for linear and clustered corrupted datasets. Also, a novel PAACDA algorithm is proposed which outperforms other unsupervised learning benchmarks on 15 popular baselines including K-means clustering, Isolation forest and LOF (Local Outlier Factor) with an accuracy of 96.35% for clustered data and 99.04% for linear data. This article also conducts a thorough exploration of the relevant literature from the previously stated perspectives. In this research work, we pinpoint all the shortcomings of the present techniques and draw direction for future work in this field.

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A Decoupled and Linear Framework for Global Outlier Rejection over Planar Pose Graph

Gao

2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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Rotational Outlier Identification in Pose Graphs using Dual Decomposition

Cited by 4 publications

References 46 publications

Mind the Gap: Norm-Aware Adaptive Robust Loss for Multivariate Least-Squares Problems

Mind the Gap: Norm-Aware Adaptive Robust Loss for Multivariate Least-Squares Problems

PAACDA: Comprehensive Data Corruption Detection Algorithm

A Decoupled and Linear Framework for Global Outlier Rejection over Planar Pose Graph

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