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.

LiDAR-inertial SLAM: Scan Context + LIO-SAM

A 3D point cloud descriptor for place recognition

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 🚁 annotates 1652 buildings in 72 universities around the world.

AnyLoc: Universal Visual Place Recognition (RA-L 2023)

Robust LiDAR SLAM with a versatile plug-and-play loop closing and pose-graph optimization.

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

Official code for CVPR 2022 paper "Rethinking Visual Geo-localization for Large-Scale Applications"

[ECCV-2020 (spotlight)] Self-supervising Fine-grained Region Similarities for Large-scale Image Localization. 🌏 PyTorch open-source toolbox for image-based localization (place recognition).

Place recognition with WiFi fingerprints using Autoencoders and Neural Networks

[RAL/IROS 2022] OverlapTransformer: An Efficient and Yaw-Angle-Invariant Transformer Network for LiDAR-Based Place Recognition.

MixVPR: Feature Mixing for Visual Place Recognition (WACV 2023)

Real-time Radar SLAM: ORORA + ScanContext

Official code for CVPR 2022 (Oral) paper "Deep Visual Geo-localization Benchmark"

Convolutional Autoencoder for Loop Closure

Implementation of IROS20 paper - "Semantic Graph Based Place Recognition for 3D Point Clouds"