Loop Closure Detection

18 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 )

Most implemented papers

DXSLAM: A Robust and Efficient Visual SLAM System with Deep Features

ivipsourcecode/dxslam 12 Aug 2020

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 29 Sep 2020

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

LiliMeng/btrf 22 Oct 2017

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

SunLoveSheep/Sem-LSD 14 Sep 2019

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 25 Nov 2019

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

wang-chen/thesis_template_ntu 6 Dec 2019

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

wh200720041/iscloam 12 Mar 2020

Loop closure detection is an essential and challenging problem in simultaneous localization and mapping (SLAM).

Place Recognition in Forests with Urquhart Tessellations

gnardari/urquhart 23 Sep 2020

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

ZhangXiwuu/Awesome_visual_place_recognition_datasets 28 Nov 2020

Visual place recognition has attracted widespread research interest in multiple fields such as computer vision and robotics.

NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation

dachengxiaocheng/NDT-Transformer 23 Mar 2021

Benefiting from the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with both geometrical and contextual information.