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
Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency
Then, we propose a graph matching approach to select correspondence objects based on the structure layout and semantic property similarity of vertices' neighbors.
ORCHNet: A Robust Global Feature Aggregation approach for 3D LiDAR-based Place recognition in Orchards
cybonic/orchnet
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In this work, we address the place recognition problem in orchards resorting to 3D LiDAR data, which is considered a key modality for robustness.
Region Prediction for Efficient Robot Localization on Large Maps
In topological SLAM the recognition takes place by comparing a signature (or feature vector) associated to the current node with the signatures of the nodes in the known map.
Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose Estimation
This paper proposes \textit{Contour Context}, a simple, effective, and efficient topological loop closure detection pipeline with accurate 3-DoF metric pose estimation, targeting the urban utonomous driving scenario.
General Place Recognition Survey: Towards the Real-world Autonomy Age
A summary of this work and our datasets and evaluation API is publicly available to the robotics community at: https://github. com/MetaSLAM/GPRS.
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.
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.
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.
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.
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.
