pySLAM is a hybrid Python/C++ Visual SLAM pipeline supporting monocular, stereo, and RGB-D cameras. It provides a broad set of modern local and global feature extractors, multiple loop-closure strategies, a volumetric reconstruction module, integrated depth-prediction models, and semantic segmentation capabilities for enhanced scene understanding.

A 3D point cloud descriptor for place recognition

The tight integration of FAST-LIO with Radius-Search-based loop closure module.

Python implementation of SLAM algorithm Stereo-PTAM

slamplay is a collection of powerful tools to start playing and experimenting with SLAM in C++

ICRA 2021 - Robust Place Recognition using an Imaging Lidar

ROS 2 package of tightly-coupled lidar inertial ndt/gicp slam

[3DV 2025, Oral] LoopSplat: Loop Closure by Registering 3D Gaussian Splats

Fisheye version of VINS-Fusion

A SLAM implementation combining FAST-LIO2 with pose graph optimization and loop closing based on Quatro and Nano-GICP

graph-theoretic framework for robust pairwise data association

A Map-based localization implementation combining FAST-LIO2 as an odometry with Quatro + Nano-GICP as a map matching method

SlideSLAM: Sparse, Lightweight, Decentralized Metric-Semantic SLAM for Multi-Robot Navigation

[ICRA'23] Official code repo for "Contour Context: Abstract Structural Distribution for 3D LiDAR Loop Detection and Metric Pose Estimation"

Convolutional Autoencoder for Loop Closure

SLAM package using NDT registration library of Autoware with loop-closure detection (odometry based) referenced from lego_loam.

Graph-based image sequences matching for the visual place recognition in changing environments.

A SLAM implementation combining FAST-LIO2 with pose graph optimization and loop closing based on ScanContext, Quatro, and Nano-GICP

Neural Graph Map: Dense Mapping with Efficient Loop Closure Integration

Merge superpoint、lightglue、MixVPR into VINS-FUSION for loop closure with TensorRT