On a New Outlier Rejection Technique

2007 ◽  
Author(s):  
C. Thibeault
Keyword(s):  
Outlier Rejection

In Defence of RANSAC for Outlier Rejection in Deformable Registration

2012 ◽  
pp. 274-287 ◽  
Author(s):  
Quoc-Huy Tran ◽  
Tat-Jun Chin ◽  
Gustavo Carneiro ◽  
Michael S. Brown ◽  
David Suter
Keyword(s):  
Deformable Registration ◽  
Outlier Rejection

scholarly journals Evaluation of DISORDER: retrospective image motion correction for volumetric brain MRI in a pediatric setting

2020 ◽  
Author(s):  
Katy Vecchiato ◽  
Alexia Egloff ◽  
Olivia Carney ◽  
Ata Siddiqui ◽  
Emer Hughes ◽  
...  
Keyword(s):  
Image Quality ◽  
Motion Correction ◽  
Spin Echo ◽  
Brain Mri ◽  
Quality Metrics ◽  
Image Motion ◽  
Reference Image ◽  
Outlier Rejection ◽  
Image Quality Metrics ◽  
Correction Technique

Background and Purpose: Head motion causes image degradation in brain MRI examinations, negatively impacting image quality, especially in pediatric populations. Here, we used a retrospective motion correction technique in children and assessed image quality improvement for 3D MRI acquisitions. Material and Methods: We prospectively acquired brain MRI at 3T using 3D sequences, T1-weighted MPRAGE, T2-weighted Turbo Spin Echo and FLAIR, in 32 unsedated children, including 7 with epilepsy (age range 2-18 years). We implemented a novel motion correction technique: Distributed and Incoherent Sample Orders for Reconstruction Deblurring using Encoding Redundancy (DISORDER). For each subject and modality, we obtained 3 reconstructions: as acquired (Aq), after DISORDER motion correction (Di), and Di with additional outlier rejection (DiOut). We analyzed 288 images quantitatively, measuring 2 objective no-reference image quality metrics: Gradient Entropy (GE) and MPRAGE White Matter Homogeneity (WM-H). As a qualitative metric, we presented blinded and randomized images to 2 expert neuroradiologists who scored them for clinical readability. Results: Both image quality metrics improved after motion correction for all modalities and improvement correlated with the amount of intrascan motion. Neuroradiologists also considered the motion corrected images as of higher quality (Wilcoxon z=-3.164 MPRAGE, z=-2.066 TSE, z=-2.645 FLAIR, for all p<0.05). Conclusions: Retrospective image motion correction with DISORDER increased image quality both from an objective and qualitative perspective. In 75% of sessions, at least one sequence was improved by this approach, indicating the benefit of this technique in un-sedated children for both clinical and research environments.

TSLAM: Tethered simultaneous localization and mapping for mobile robots

2017 ◽  
Vol 36 (12) ◽  
pp. 1363-1386 ◽  
Author(s):  
Patrick McGarey ◽  
Kirk MacTavish ◽  
François Pomerleau ◽  
Timothy D Barfoot
Keyword(s):  
Mobile Robots ◽  
Particle Filter ◽  
Low Cost ◽  
Simultaneous Localization And Mapping ◽  
Ground Truth ◽  
Anchor Point ◽  
Outlier Rejection ◽  
Cluttered Environments ◽  
Localization And Mapping ◽  
Anchor Points

Tethered mobile robots are useful for exploration in steep, rugged, and dangerous terrain. A tether can provide a robot with robust communications, power, and mechanical support, but also constrains motion. In cluttered environments, the tether will wrap around a number of intermediate ‘anchor points’, complicating navigation. We show that by measuring the length of tether deployed and the bearing to the most recent anchor point, we can formulate a tethered simultaneous localization and mapping (TSLAM) problem that allows us to estimate the pose of the robot and the positions of the anchor points, using only low-cost, nonvisual sensors. This information is used by the robot to safely return along an outgoing trajectory while avoiding tether entanglement. We are motivated by TSLAM as a building block to aid conventional, camera, and laser-based approaches to simultaneous localization and mapping (SLAM), which tend to fail in dark and or dusty environments. Unlike conventional range-bearing SLAM, the TSLAM problem must account for the fact that the tether-length measurements are a function of the robot’s pose and all the intermediate anchor-point positions. While this fact has implications on the sparsity that can be exploited in our method, we show that a solution to the TSLAM problem can still be found and formulate two approaches: (i) an online particle filter based on FastSLAM and (ii) an efficient, offline batch solution. We demonstrate that either method outperforms odometry alone, both in simulation and in experiments using our TReX (Tethered Robotic eXplorer) mobile robot operating in flat-indoor and steep-outdoor environments. For the indoor experiment, we compare each method using the same dataset with ground truth, showing that batch TSLAM outperforms particle-filter TSLAM in localization and mapping accuracy, owing to superior anchor-point detection, data association, and outlier rejection.

scholarly journals Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection

2020 ◽  
Vol 5 (2) ◽  
pp. 1127-1134 ◽  
Author(s):  
Heng Yang ◽  
Pasquale Antonante ◽  
Vasileios Tzoumas ◽  
Luca Carlone
Keyword(s):  
Spatial Perception ◽  
Outlier Rejection
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