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About 6,647 papers for “loop closure”

Loop Closure

Liz Chrastil · OSF Preprints (OSF Preprints) · 2024

Data and analysis scripts for the Loop Closure Task

Cited by 0

Loop Closure

Liz Chrastil · OSF Preprints (OSF Preprints) · 2019

Cited by 0

Loop closure potential.

Nils Woetzel (118386), Mert Karakaş (118390), Rene Staritzbichler (118393) et al. · Figshare · 2015

<p>A describes two β-strands connected by a loop characterized by the Euclidean distance between the two ends and the number of residues in the loop connecting those two ends. B describes the derived energy potential, where the energy is a function of the number of residues in the loop and the Euclidean distance between the ends of the main axes.</p>

Cited by 0Open access

Loop Ileostomy Closure

Zhuqing Zhou, Zhe Zhu, Jiaqi Jiang · Diseases of the Colon & Rectum · 2023

Department of Colorectal Surgery, Shanghai East Hospital, School of Medicine, Tongji University, China Funding/Support: None reported. Financial Disclosures: None reported. Correspondence: Zhe Zhu, M.D., Department of Colorectal Surgery, Shanghai East Hospital, Tongji University, 200120, No. 150, Jimo Road, Pudong New Area, Shanghai, China. Email: [email protected]

Cited by 0

IHEP Recursive Loop Closure

Jason M Jarmacz, Trade Momentum LLC · Zenodo (CERN European Organization for Nuclear Research) · 2025

This document specifies the Recursive Loop Closure (RLC) mechanism that bridges the gap between IHEP's AI-driven interventions and real-world patient outcomes, creating a morphogenetic feedback system that continuously refines the digital twin framework based on actual clinical and behavioral data.

Cited by 0Open access

IHEP Recursive Loop Closure

Jason M Jarmacz, Trade Momentum LLC · Zenodo (CERN European Organization for Nuclear Research) · 2025

This document specifies the Recursive Loop Closure (RLC) mechanism that bridges the gap between IHEP's AI-driven interventions and real-world patient outcomes, creating a morphogenetic feedback system that continuously refines the digital twin framework based on actual clinical and behavioral data.

Cited by 0Open access

Loop closure procedure in

Swanand P Gore, Karmali, Anjum M, Blundell, Tom L · Figshare · 2011

<b>Copyright information:</b>Taken from ": a versatile engine for discrete restraint-based conformational sampling of macromolecules"BMC Structural Biology 2007;7():13-13.Published online 21 Mar 2007PMCID:PMC1847436. lies on a circle centered at the mid-point of line joining and . Radius of the circle is determined by length of the line. The circle is in a plane perpendicular to the line. Candidate positions are sampled on this circle and {, , } – {, , } mapping (explained earlier in relation to guided sampling) is used to select a position.

Cited by 0Open access

Loop closure procedure in

Swanand P Gore, Karmali, Anjum M, Blundell, Tom L · Figshare · 2011

<b>Copyright information:</b>Taken from ": a versatile engine for discrete restraint-based conformational sampling of macromolecules"BMC Structural Biology 2007;7():13-13.Published online 21 Mar 2007PMCID:PMC1847436. lies on a circle centered at the mid-point of line joining and . Radius of the circle is determined by length of the line. The circle is in a plane perpendicular to the line. Candidate positions are sampled on this circle and {, , } – {, , } mapping (explained earlier in relation to guided sampling) is used to select a position.

Cited by 0Open access

Flowchart of loop closure judgment.

Zhuoheng Xiang (20485194), Jiaxi Guo (13866917), Jin Meng (129380) et al. · Figshare · 2024

&lt;div&gt;&lt;p&gt;Accurate localization is a critical technology for the application of intelligent robots and automation systems in complex indoor environments. Traditional visual SLAM (Simultaneous Localization and Mapping) techniques often face challenges with localization accuracy in high similarity scenes. To address this issue, this paper proposes an improved visual SLAM loop closure detection algorithm that integrates deep learning techniques. Using the TUM f3 loh, Lip6 Indoor, and Bicocca Indoor datasets as experimental bases, a detailed comparison of the proposed algorithm against other methods was conducted across various evaluation metrics. The experimental results show that the proposed loop closure detection algorithm significantly outperforms traditional methods in terms of localization accuracy in high similarity scenes. Specifically, the detection accuracy rates for the TUM f3 loh, Lip6 Indoor, and Bicocca Indoor datasets were 66.67%, 72.72%, and 80.00%, respectively, representing an approximate 18% improvement over the average accuracy of ORB-SLAM2. Additionally, the proposed method demonstrated excellent performance in trajectory error, with a root mean square error (RMSE) of just 0.0816m on the Bicocca Indoor dataset, significantly lower than the 0.1341m RMSE of ORB-SLAM2. Furthermore, improvements in feature extraction and matching mechanisms greatly reduced the occurrence of mismatches, enhancing the system’s adaptability for more accurate localization and navigation in complex indoor environments. The proposed method effectively enhances localization accuracy and system practicality in visually similar indoor environments, offering a new direction for the development of visual SLAM technology and holding significant application potential in intelligent robots and indoor navigation systems.&lt;/p&gt;&lt;/div&gt;

Cited by 0Open access

Loop Closure using laser 2D

Pujol Badell, Sergi · DIGITAL.CSIC (Spanish National Research Council (CSIC)) · 2021

Trabajo fin de máster presentado en la Universidad Politécnica de Cataluña, Máster en Automática y Robótica.--2021-10-08

Cited by 0Open access

Comparison of loop closure methods.

Steven M. Lewis (217213), Brian A. Kuhlman (217219) · Figshare · 2015

&lt;p&gt;This table demonstrates the rough equality of results from KIC, CCD, and combined loop sampling. The default protocol columns are the same as in Table RMSD; the CCD and KIC columns were generated using either loop modeling type alone. Each column represents one of the RMSD metrics described in the Results. Each of the loop modeling choices is broadly equivalent. Notice that structure 3cgc has in lower RMSDs under the combined protocol than either loop sampling type alone; this is a falsely optimistic interpretation because AnchoredDesign is never correctly predicting 3cgc under any of these protocols. The structures produced for 3cgc are isoenergetic and it is coincidental that the combined protocol happens to have lower RMSDs.&lt;/p&gt;

Cited by 0Open access

AirLoop: Lifelong Loop Closure Detection

Dasong Gao, Chen Wang, Sebastian Scherer · 2022 International Conference on Robotics and Automation (ICRA) · 2022

Loop closure detection is an important building block that ensures the accuracy and robustness of simultaneous localization and mapping (SLAM) systems. Due to their generalization ability, CNN-based approaches have received increasing attention. Although they normally benefit from training on datasets that are diverse and reflective of the environments, new environments often emerge after the model is deployed. It is therefore desirable to incorporate the data newly collected during operation for incremental learning. Nevertheless, simply finetuning the model on new data is infeasible since it may cause the model's performance on previously learned data to degrade over time, which is also known as the problem of catastrophic forgetting. In this paper, we present AirLoop, a method that leverages techniques from lifelong learning to minimize forgetting when training loop closure detection models incrementally. We experimentally demonstrate the effectiveness of AirLoop on TartanAir, Nordland, and RobotCar datasets. To the best of our knowledge, AirLoop is one of the first works to achieve lifelong learning of deep loop closure detectors.

Cited by 17Open access