There are 10 repositories under place-recognition topic.
A general framework for map-based visual localization. It contains 1) Map Generation which support traditional features or deeplearning features. 2) Hierarchical-Localizationvisual in visual(points or line) map. 3)Fusion framework with IMU, wheel odom and GPS sensors.
Code for the CVPR2021 paper "Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition"
LiDAR SLAM = FAST-LIO + Scan Context
ACM Multimedia2020 University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization :helicopter: annotates 1652 buildings in 72 universities around the world.
ICRA 2021 - Robust Place Recognition using an Imaging Lidar
A list of papers about point cloud based place recognition, also known as loop closure detection in SLAM (processing)
PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition, CVPR 2018
Place recognition with WiFi fingerprints using Autoencoders and Neural Networks
MixVPR: Feature Mixing for Visual Place Recognition (WACV 2023)
Official code for CVPR 2022 (Oral) paper "Deep Visual Geo-localization Benchmark"
Radar SLAM: yeti radar odometry + scan context
[ICRA 2021] This repository contains the code for "Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling".
MinkLoc++: Lidar and Monocular Image Fusion for Place Recognition
The Official Deep Learning Framework for Robot Place Learning
[ICRA 2022] The official repository for "LoGG3D-Net: Locally Guided Global Descriptor Learning for 3D Place Recognition", In 2022 International Conference on Robotics and Automation (ICRA), pp. 2215-2221.
Automatic download VPR datasets in a standard format
A curated list of Visual Place Recognition (VPR)/ loop closure detection (LCD) datasets
Differentiable Scan Context with Orientation
🏞️ [IEEE ICRA2023] The official repository for paper "Wild-Places: A Large-Scale Dataset for Lidar Place Recognition in Unstructured Natural Environments" To appear in 2023 IEEE International Conference on Robotics and Automation (ICRA)
Easily download and evaluate pre-trained Visual Place Recognition methods. Code built for the ICCV 2023 paper "EigenPlaces: Training Viewpoint Robust Models for Visual Place Recognition"
MinkLoc3Dv2: Improving Point Cloud Based Place Recognition with Ranking-based Loss and Large Batch Training
Benchmarking and evaluation framework for place recognition methods, featuring SuperPoint+SuperGlue, LoGG3D-Net, Scan Context, DBoW2, MixVPR, STD
Light-weight place recognition and loop detection using road markings