Loop Closure Detection
25 papers with code • 0 benchmarks • 3 datasets
Loop closure detection is the process of detecting whether an agent has returned to a previously visited location.
( Image credit: Backtracking Regression Forests for Accurate Camera Relocalization )
Benchmarks
These leaderboards are used to track progress in Loop Closure Detection
KAIST Urban
New College
AUT-VI
Most implemented papers
LCDNet: Deep Loop Closure Detection and Point Cloud Registration for LiDAR SLAM
robot-learning-freiburg/LCDNet
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Loop closure detection is an essential component of Simultaneous Localization and Mapping (SLAM) systems, which reduces the drift accumulated over time.
NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation
dachengxiaocheng/NDT-Transformer
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Benefiting from the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with both geometrical and contextual information.
AttDLNet: Attention-based DL Network for 3D LiDAR Place Recognition
cybonic/attdlnet
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LiDAR-based place recognition is one of the key components of SLAM and global localization in autonomous vehicles and robotics applications.
Probabilistic Appearance-Invariant Topometric Localization with New Place Awareness
Probabilistic state-estimation approaches offer a principled foundation for designing localization systems, because they naturally integrate sequences of imperfect motion and exteroceptive sensor data.
On the descriptive power of LiDAR intensity images for segment-based loop closing in 3-D SLAM
We propose an extension to the segment-based global localization method for LiDAR SLAM using descriptors learned considering the visual context of the segments.
AirLoop: Lifelong Loop Closure Detection
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.
CT-ICP: Real-time Elastic LiDAR Odometry with Loop Closure
Multi-beam LiDAR sensors are increasingly used in robotics, particularly with autonomous cars for localization and perception tasks, both relying on the ability to build a precise map of the environment.
Loop closure detection using local 3D deep descriptors
We compare our L3D-based loop closure approach with recent approaches on LiDAR data and achieve state-of-the-art loop closure detection accuracy.
Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition
We propose a novel approach for improving image retrieval based on previously trained models.
Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination Systems
In this paper, we propose a phase based Simultaneous Localization and Mapping (Phase-SLAM) framework for fast and accurate SLI sensor pose estimation and 3D object reconstruction.
