ORB_SLAM3
Kalman-and-Bayesian-Filters-in-Python
ORB_SLAM3 | Kalman-and-Bayesian-Filters-in-Python | |
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15 | 32 | |
6,031 | 15,859 | |
2.4% | - | |
0.0 | 0.0 | |
20 days ago | 3 months ago | |
C++ | Jupyter Notebook | |
GNU General Public License v3.0 only | GNU General Public License v3.0 or later |
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ORB_SLAM3
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How to make bot that can navigate to instructed positions in a 3d world using only rgb images
Thank you for the recommendation. Other's I've talked to have also mentioned slam. I think I might use this repo that looks promising: https://github.com/UZ-SLAMLab/ORB_SLAM3
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Bot that can navigate to instructed positions in a 3d world
https://github.com/UZ-SLAMLab/ORB_SLAM3 looks promising. Thank you for the recommendation!
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Any links/tutorials on how to integrate a mono cam with ros noetic for slam?
ORB_SLAM3 on GitHub: https://github.com/UZ-SLAMLab/ORB_SLAM3
- How to implement SLAM from scratch in C++
- ORB-SLAM3 memory leak
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Wiring in progress
Yeah pretty much. I'd say it can still be useful if your custom application only needs one or two new modules and can otherwise be formed from existing code. It's great if you make a lot of different systems with similar sub-modules. The bigger research institutions also open source a lot of their code with ROS integrations, so you can just drop in state of the art modules where appropriate. e.g. OrbSlam3,
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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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What's the current SOTA for vSLAM?
I hear good things about stereo-visual + inertial and OrbSlam3 seems pretty hot, but this is a couple years old now and I'm not active enough in the field to give a definitive opinion.
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Advice regarding software for indoor autonomous MAV/drone
I'm currently in the planning stage of building an indoor autonomous MAV/drone. Up until this point I thought this would require a SLAM solution, such as ORB-SLAM3, but now I see this framework by Intel which doesn't mention SLAM anywhere. All of the literature seems to be about SLAM so maybe I just don't know the terminology for what I'm looking for.
- Software advice for inside-out cave mapping
Kalman-and-Bayesian-Filters-in-Python
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The Kalman Filter
A fantastic interactive introduction to Kalman filters can be found on the following repo:
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...
It explains them from first principles and provides the intuitive rationale for them but doesn't shy away from the math when it feels the student should be ready for it.
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Kalman Filter Explained Simply
No thread on Kalman Filters is complete without a link to this excellent learning resource, a book written as a set of Jupyter notebooks:
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...
That book mentions alpha-beta filters as sort of a younger sibling to full-blown Kalman filters. I recently had need of something like this at work, and started doing a bunch of reading. Eventually I realized that alpha-beta filters (and the whole Kalman family) is very focused on predicting the near future, whereas what I really needed was just a way to smooth historical data.
So I started reading in that direction, came across "double exponential smoothing" which seemed perfect for my use-case, and as I went into it I realized... it's just the alpha-beta filter again, but now with different names for all the variables :(
I can't help feeling like this entire neighborhood of math rests on a few common fundamental theories, but because different disciplines arrived at the same systems via different approaches, they end up sounding a little different and the commonality is obscured. Something about power series, Euler's number, gradient descent, filters, feedback systems, general system theory... it feels to me like there's a relatively small kernel of intuitive understanding at the heart of all that stuff, which could end up making glorious sense of a lot of mathematics if I could only grasp it.
Somebody help me out, here!
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Recommendations for undergrad to learn optimal state estimation
This provides an excellent intro that jumps right into code. https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python
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A Non-Mathematical Introduction to Kalman Filters for Programmers
If you know a bit of Python and you find it sometimes tough to grind through a textbook, take a look here:
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...
Interactive examples programmed in Jupyter notebooks.
- Looking for a study partner to learn kalman filter
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Kalman Filter for Beginners
Thank you, very good resource! Timely too, as I am revising this topic.
My work is mostly in python. I found this interactive book using Jupyter that explains Kalman filters from first principles.
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Pyt...
- Starting out with Kalman Filter.
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want to learn kalman filter
Try this book
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kalman filter & c++
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python And on robotics in general
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Do you use particle/Kalman filters at work?
- Kalman and Bayesian Filters in Python
What are some alternatives?
openvslam - OpenVSLAM: A Versatile Visual SLAM Framework
30-days-of-elixir - A walk through the Elixir language in 30 exercises.
open_vins - An open source platform for visual-inertial navigation research.
clojure-style-guide - A community coding style guide for the Clojure programming language
SuperGluePretrainedNetwork - SuperGlue: Learning Feature Matching with Graph Neural Networks (CVPR 2020, Oral)
git-internals-pdf - PDF on Git Internals
Udacity-self-driving-car-engineer-P6-Kidnapped-Vehicle - 优达学城无人驾驶工程师纳米学位P6--被绑架的汽车--定位
kalmanpy - Implementation of Kalman Filter in Python
VINS-Mono - A Robust and Versatile Monocular Visual-Inertial State Estimator
react-bits - ✨ React patterns, techniques, tips and tricks ✨
vortex-auv - Software for guidance, navigation and control for the Vortex AUVs. Purpose built for competing in AUV/ROV competitions.
elm-architecture-tutorial - How to create modular Elm code that scales nicely with your app