Loop Closure Detection
24 papers with code • 0 benchmarks • 2 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
New College
KAIST Urban
Most implemented papers
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.
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.
Backtracking Regression Forests for Accurate Camera Relocalization
Camera relocalization plays a vital role in many robotics and computer vision tasks, such as global localization, recovery from tracking failure, and loop closure detection.
Sem-LSD: A Learning-based Semantic Line Segment Detector
Combined with high-level semantics, Sem-LS is more robust under cluttered environment compared with existing line-shaped representations.
Fast and Incremental Loop Closure Detection Using Proximity Graphs
AnshanTJU/FILD
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Visual loop closure detection, which can be considered as an image retrieval task, is an important problem in SLAM (Simultaneous Localization and Mapping) systems.
Kernel learning for visual perception
To this end, the novel kernel learning methods for several basic visual perceptual tasks, including object tracking, localization, mapping, and image recognition, are proposed and demonstrated both theoretically and practically.
Intensity Scan Context: Coding Intensity and Geometry Relations for Loop Closure Detection
Loop closure detection is an essential and challenging problem in simultaneous localization and mapping (SLAM).
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.
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.
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.
