SuperPoint_SLAM
slambook-en
SuperPoint_SLAM | slambook-en | |
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2 | 11 | |
504 | 1,374 | |
- | - | |
1.8 | 0.0 | |
about 3 years ago | about 1 year ago | |
C++ | TeX | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 only |
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SuperPoint_SLAM
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Modular Open Source Visual SLAM
Hi everyone, I am trying to implement a VSLAM with DNN specifically the Feature Extraction module in the SLAM pipeline. Something on the lines of this repo Superpoint_SLAM , which integrates SuperPoint Feature extraction into ORB_SLAM2
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Complete Open Source Deep Learning Implementations For V-SLAM
As you've mentioned, there are many papers on deep local feature extraction, like SuperPoint and R2D2. If you wish to use them in SLAM, you can simply replace the feature extraction module in the existing SLAM system with the deep local feature method. An example is shown here - this system uses SuperPoint as local features instead of ORB features in the original ORB-SLAM 2 pipeline. https://github.com/KinglittleQ/SuperPoint_SLAM
slambook-en
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How to implement SLAM from scratch in C++
Looking at code repos is not an effective way to learn in my opinion. I highly recommend this open text book that has practical explained and many code examples https://github.com/gaoxiang12/slambook-en
- SLAM localization methods?
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Trying to understand the complications of including geometric priors in SLAM
According to a popular slam book(https://github.com/gaoxiang12/slambook-en/blob/master/slambook-en.pdf) this has to do with solving the Hx=g with schur trick and how row operations induce some implicit constraints during the marginalization step ( pg 211~213). However, I don't think I am getting the full picture here.
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Starting point for learning VSLAM?
Hi, I work on a tangentially related topic and the best resource I've found so far is this book: https://github.com/gaoxiang12/slambook-en/blob/master/slambook-en.pdf. I went through some chapters only, but everything seems to be explained clearly and with code examples. Hope this helps!
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Clarification for Lie Algebra
I am trying to understand the Lie algebra for robotics and reading up this: https://github.com/gaoxiang12/slambook-en/blob/master/slambook-en.pdf
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Mono SLAM -Camera pose on Mobile Robot
If you're wanting to learn how all this stuff works I highly recommend this open source book: https://github.com/gaoxiang12/slambook-en
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Literature request: Lie groups and algebra
Hey! I was in your shoes a couple months ago! The YouTube video that the_remainder posted is how I wrapped my mind around lie algebra, but in order for me to be able to use it effectively took going through the relevant chapter in the slambook (https://github.com/gaoxiang12/slambook-en)
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C++ or Python (SLAM)
Great book to learn slam https://github.com/gaoxiang12/slambook-en
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Online course for SLAM with assignments
https://github.com/gaoxiang12/slambook-en I would recommend this.
- Roadmap to study Visual-SLAM
What are some alternatives?
rtabmap - RTAB-Map library and standalone application
symforce - Fast symbolic computation, code generation, and nonlinear optimization for robotics
orb_slam_2_ros - A ROS implementation of ORB_SLAM2
ORB_SLAM2 - Real-Time SLAM for Monocular, Stereo and RGB-D Cameras, with Loop Detection and Relocalization Capabilities
openvslam - OpenVSLAM: A Versatile Visual SLAM Framework
ORB_SLAM - A Versatile and Accurate Monocular SLAM
open_vins - An open source platform for visual-inertial navigation research.
Mini-SLAM_student - Student version of Mini-SLAM.
pyslam - pySLAM contains a monocular Visual Odometry (VO) pipeline in Python. It supports many modern local features based on Deep Learning.
rpg_svo - Semi-direct Visual Odometry
maplab - A Modular and Multi-Modal Mapping Framework
ORB_SLAM3 - ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM