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 with no code
MoD-SLAM: Monocular Dense Mapping for Unbounded 3D Scene Reconstruction
Monocular SLAM has received a lot of attention due to its simple RGB inputs and the lifting of complex sensor constraints.
DDN-SLAM: Real-time Dense Dynamic Neural Implicit SLAM
To address dynamic tracking interferences, we propose a feature point segmentation method that combines semantic features with a mixed Gaussian distribution model.
Attacking the Loop: Adversarial Attacks on Graph-based Loop Closure Detection
With the advancement in robotics, it is becoming increasingly common for large factories and warehouses to incorporate visual SLAM (vSLAM) enabled automated robots that operate closely next to humans.
RadarLCD: Learnable Radar-based Loop Closure Detection Pipeline
The methodology undergoes evaluation across a variety of FMCW Radar dataset scenes, and it is compared to state-of-the-art systems such as Scan Context for Place Recognition and ICP for Loop Closure.
TReR: A Lightweight Transformer Re-Ranking Approach for 3D LiDAR Place Recognition
Autonomous driving systems often require reliable loop closure detection to guarantee reduced localization drift.
LSGDDN-LCD: An Appearance-based Loop Closure Detection using Local Superpixel Grid Descriptors and Incremental Dynamic Nodes
Loop Closure Detection (LCD) is an essential component of visual simultaneous localization and mapping (SLAM) systems.
GraffMatch: Global Matching of 3D Lines and Planes for Wide Baseline LiDAR Registration
Using geometric landmarks like lines and planes can increase navigation accuracy and decrease map storage requirements compared to commonly-used LiDAR point cloud maps.
A Faster, Lighter and Stronger Deep Learning-Based Approach for Place Recognition
We designed RepVGG-lite as the backbone network in our architecture, it is more discriminative than other general networks in the Place Recognition task.
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition
In recent years, deep learning brings improvements to place recognition by learnable feature extraction.
Fusing Event-based Camera and Radar for SLAM Using Spiking Neural Networks with Continual STDP Learning
This work proposes a first-of-its-kind SLAM architecture fusing an event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for drone navigation.
