open_vins
VINS-Mono
open_vins | VINS-Mono | |
---|---|---|
5 | 1 | |
1,996 | 4,754 | |
2.3% | 1.8% | |
6.9 | 2.8 | |
4 months ago | 19 days ago | |
C++ | C++ | |
GNU General Public License v3.0 only | GNU General Public License v3.0 only |
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open_vins
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Modular Open Source Visual SLAM
From what I have understood after reading research papers related to the VSLAM, the modularity aspect is not easy to achieve given the extracted features and descriptors are intrinsically linked with feature matching and handling of map points. I would like to know if there are some good Open Source VSLAM projects available which can be used with different feature extractors so I can get a comparative results with respect to just changing the feature extractors . I have tried pyslam project which is actually quite good considering the modularity but as the author himself points out this is only for academic purposes and when I compared the results of ORB_SLAM2 feature extractor using this module vs the original ORB_SLAM2 for KITTI data set , the results are not comparable. I am also looking into OpenVINS ( and from initial reading it is also using ORB Features, although it does have a base Tracker class which can be modified to create a new Tracker with different descriptor) If anyone has worked with custom feature extractor incorporated into prebuilt SLAM pipeline and can guide me as to how to proceed with the implementation of custom Feature extractor into a SLAM Front end using a Open Source VSLAM framework, it will be really helpful.
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SLAM vs. Visual Odometry Approaches
Because the standard MSCKF is the only one that doesn't contain the map points in the state. Note that this is only for the standard MSCKF. More modern MSCKFS variations like OpenVINS will actually add some SLAM features because it improves the accuracy.
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Advances in SLAM since 2016
Aside from that there have been some publications of some high quality open source SLAM systems like OpenVINS and ORB-SLAM3.
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Sfm or slam pseudo code
Check out open vins. Its an implementation of the vins slam project. https://github.com/rpng/open_vins
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Visual Odometry or SLAM with pose uncertainty output
Generally you want to use a Kalman Filter based method if you want access to the uncertainties. This is because it is much easier to extract a subset of the covariance in the kalman filter form. I would recommend OpenVins. One of the best open source visual odometry projects, and it is pretty well documented.
VINS-Mono
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Doing SLAM with laptop sensors
Try this https://github.com/HKUST-Aerial-Robotics/VINS-Mono
What are some alternatives?
ORB_SLAM3 - ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
rtabmap - RTAB-Map library and standalone application
Kimera - Index repo for Kimera code
openvslam - OpenVSLAM: A Versatile Visual SLAM Framework
R-VIO - Robocentric Visual-Inertial Odometry
msckf_vio - Robust Stereo Visual Inertial Odometry for Fast Autonomous Flight
dv
xivo - X Inertial-aided Visual Odometry
SuperPoint_SLAM - SuperPoint + ORB_SLAM2
Kalman-and-Bayesian-Filters-in-Python - Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions.