Kimera
VINS-Mono
Kimera | VINS-Mono | |
---|---|---|
3 | 1 | |
1,721 | 4,754 | |
4.4% | 1.8% | |
0.0 | 2.8 | |
over 3 years ago | 16 days ago | |
C++ | ||
BSD 2-clause "Simplified" License | GNU General Public License v3.0 only |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Kimera
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Doing SLAM with laptop sensors
You could try your luck with OrbSLAM3 or Kimera, they're pretty close to state of the art and open source. Might have a bit of a learning curve though.
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How to reduce points in VSLAM to 3D meshes? Has it been done before?
Kimera is the one SLAM algorithm that is probably most similar to what you are interested in because it deals with meshes. So the output reconstruction is a 3D mesh map.
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Fuse between segmentation and 3D model
Have a look at Kimera
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?
rtabmap - RTAB-Map library and standalone application
ORB_SLAM3 - ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM
openvslam - OpenVSLAM: A Versatile Visual SLAM Framework
R-VIO - Robocentric Visual-Inertial Odometry
awesome-robotics-libraries - :sunglasses: A curated list of robotics libraries and software
dv
xivo - X Inertial-aided Visual Odometry
visual-slam-roadmap - Roadmap to become a Visual-SLAM developer in 2023
open_vins - An open source platform for visual-inertial navigation research.
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.