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A repository to keep track of Deep Learning based methods for visual odometry (pull requests are always welcome)
using SuperPoint as visual front to VINS-Mono
Merge superpoint、lightglue、MixVPR into VINS-FUSION for loop closure with TensorRT
PyTorch Implementation of DeepVO
Convolutional Neural Networks for Denoising Gyroscopes of Low-Cost IMUs(This is a quest for reporoducing the dataset)
Superpoint and Superglue training and testing code, also some traditional method (SIFT and ORB)
Config files for my GitHub profile.
ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
pySLAM contains a monocular Visual Odometry (VO) pipeline in Python. It supports many modern local features based on Deep Learning.
A simple python implemented frame-by-frame visual odometry with SuperPoint feature detector and SuperGlue feature matcher.
edition 2 of the slambook
Visual Odometry Pipeline using KITTI dataset
Visual Inertial Odometry using SuperPoint and GTSAM
SuperPoint and SuperGlue with TensorRT. Deploy with C++.
SuperPoint + ORB_SLAM2
(Work in Progress) SuperSLAM: Open Source Framework for Deep Learning based Visual SLAM
VIODE: A Simulated Dataset to Address the Challenges of Visual-Inertial Odometry in Dynamic Environments
This is an improved version of ORB-SLAM3 that adds an object detection module implemented with YOLOv5 to achieve SLAM in dynamic environments.
Joint Keypoint and Object Detection
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