This is the official repo of the project gnssFGO.

SceneLib2 is an open-source C++ library for SLAM originally designed and implemented by Professor Andrew Davison at Imperial College London.

Python binding of SLAM graph optimization framework g2o

[CVPR 2022 Oral, Best Student Paper] EPro-PnP: Generalized End-to-End Probabilistic Perspective-n-Points for Monocular Object Pose Estimation

CMU Vision-Language-Autonomy Challenge - Unity Setup

UFOMap: An Efficient Probabilistic 3D Mapping Framework That Embraces the Unknown

vertigo repos from OpenSLAM.org

A Modular and Multi-Modal Mapping Framework

我的导航算法学习笔记，内容涵盖导航定位开源程序的源码解读、开源项目梳理、书籍讲义、博客翻译、教程讲座推荐；所有内容都可以随意转载，原始文件都放在这里了，大家可以在我的基础上整理出自己的一些文档。（Tips：①主要是写给初学者，已经有基础的同学应该多看论文和代码，看我的笔记学不到啥；②仓库持续更新中，不建议 fork）

wip - a simple slam implementation so i can learn stuff

深蓝学院 手写VIO

Graduation Project: A point cloud semantic segmentation and VIO based 3D reconstruction method using RGB-D and IMU

[CVPR 2020 Oral] A differentiable framework for 3D registration

Official code for "I2D-Loc: Camera Localization via Image to LiDAR Depth Flow"

This is a repository to implement the work of ``Deep Auxiliary Learning for Visual Localization and Odometry'' known as VLocNet.

Cross-Descriptor Visual Localization and Mapping

CamOdoCal: Automatic Intrinsic and Extrinsic Calibration of a Rig with Multiple Generic Cameras and Odometry

Visual localization method in LiDAR maps. Only a stereo camera is need during localization since the LiDAR map can be built offline.

VIDO-SLAM is a Visual Inertial SLAM system for dynamic environments, and it can also estimate dynamic objects motion and track objects.

The relocalization task aims to estimate the 6-DoF pose of a novel (unseen) frame in the coordinate system given by the prior model of the world.

🔥SLAM, VIsual localization, keypoint detection, Image matching, Pose/Object tracking, Depth/Disparity/Flow Estimation, 3D-graphic, etc. related papers and code

LightGlue: Local Feature Matching at Light Speed (ICCV 2023)

[ECCV 2022] Towards Scale-Aware, Robust, and Generalizable Unsupervised Monocular Depth Estimation by Integrating IMU Motion Dynamics

My personal note about local and global descriptor

PyTorch code and models for the DINOv2 self-supervised learning method.

Official repository for the ICLR 2024 paper "Towards Seamless Adaptation of Pre-trained Models for Visual Place Recognition".