An open-source version of the Fetch charge docking system.

package for docking ros2 robots. (based off fetch open auto dock)

Automatic docking based on LiDAR and self-designed dock.

Software for iClebo Kobuki

A Robust and Versatile Monocular Visual-Inertial State Estimator

LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain

Provides ROS integration for Cartographer.

M2DGR： a Multi-modal and Multi-scenario Dataset for Ground Robots(RA-L2021 & ICRA2022)

UKPGAN: A General Self-Supervised Keypoint Detector (CVPR2022)

[ICCV 2019] Key.Net: Keypoint Detection by Handcrafted and Learned CNN Filters

DynaVINS : A Visual-Inertial SLAM for Dynamic Environments

Code for "LoFTR: Detector-Free Local Feature Matching with Transformers", CVPR 2021, T-PAMI 2022

SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)

SLAM performance evaluation framework

使用SuperPoint方法提取特征点，来代替ORB-SLAM中的ORB特征

Try to reproduce SuperPoint

The C++ implementation of SuperPoint

Resources of semantic segmantation based on Deep Learning model

Source Code of our CVPR2021 paper "Rethinking BiSeNet For Real-time Semantic Segmentation"

这是一个YoloV4-pytorch的源码，可以用于训练自己的模型。

Pytorch implementation of U-Net, R2U-Net, Attention U-Net, and Attention R2U-Net.

Real-time SLAM system with deep features

SSL_SLAM: Lightweight 3-D Localization and Mapping for Solid-State LiDAR IEEE RA-L 2021

SSL_LIO: Solid-State LiDAR based LiDAR-Inertial SLAM

SSL_SLAM2: Lightweight 3-D Localization and Mapping for Solid-State LiDAR (mapping and localization separated) ICRA 2021

SuperPoint + ORB_SLAM2

PyTorch pre-trained model for real-time interest point detection, description, and sparse tracking (https://arxiv.org/abs/1712.07629)

Efficient neural feature detector and descriptor

You can learn slam step by step，there are lot of tutorials

Detailed comments for ORB-SLAM3