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
Latest papers
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.
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.
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.
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.
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.
Visual place recognition: A survey from deep learning perspective
Visual place recognition has attracted widespread research interest in multiple fields such as computer vision and robotics.
Fast and Incremental Loop Closure Detection with Deep Features and Proximity Graphs
anshan-ar/fild
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In recent years, the robotics community has extensively examined methods concerning the place recognition task within the scope of simultaneous localization and mapping applications. This article proposes an appearance-based loop closure detection pipeline named ``FILD++" (Fast and Incremental Loop closure Detection). First, the system is fed by consecutive images and, via passing them twice through a single convolutional neural network, global and local deep features are extracted. Subsequently, a hierarchical navigable small-world graph incrementally constructs a visual database representing the robot's traversed path based on the computed global features. Finally, a query image, grabbed each time step, is set to retrieve similar locations on the traversed route. An image-to-image pairing follows, which exploits local features to evaluate the spatial information.
Place Recognition in Forests with Urquhart Tessellations
In this letter, we present a novel descriptor based on Urquhart tessellations derived from the position of trees in a forest.
DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features
For visual SLAM algorithms, though the theoretical framework has been well established for most aspects, feature extraction and association is still empirically designed in most cases, and can be vulnerable in complex environments.
Dynamic Object Tracking and Masking for Visual SLAM
introlab/dotmask
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In dynamic environments, performance of visual SLAM techniques can be impaired by visual features taken from moving objects.
