gisbi-kim

gisbi-kim / modern-robot-state-estimation

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10-lecture Beamer course on Modern Robot State Estimation — ESKF, MSCKF, FAST-LIO, factor graphs, with direct mappings to OpenVINS / FAST-LIO2 / GTSAM source code. Korean content with English math/code.

24
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100% credibility
Found May 02, 2026 at 18 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
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AI Summary

A 10-part lecture series on modern robot state estimation techniques centered on the Error-State Kalman Filter, presented as slides with an online browser viewer and downloadable PDF.

How It Works

1
🔍 Find the Lectures

You stumble upon this free course while searching for ways robots know where they are and how they move.

2
🌐 Open Online Slides

Click the link to see the full set of colorful slides right in your web browser, no setup needed.

3
📖 Pick a Lesson

Use the side menu to jump into any of the 10 sessions, from basics to advanced sensor tricks.

4
View or Save
👀
Browse Online

Scroll through slides, search text, and zoom as much as you want.

💾
Download Book

Get the complete 900-page PDF to study on your tablet or print parts.

5
💡 Connect Ideas to Robots

Follow along as lessons link math to real-world robot projects you've heard of.

🎓 Become a Pro

You finish understanding how robots precisely track their place in the world using smart math!

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AI-Generated Review

What is modern-robot-state-estimation?

This TeX-based Beamer course delivers a 10-lecture deep dive into modern robot state estimation, covering ESKF, MSCKF, FAST-LIO, FAST-LIO2, factor graphs, and GTSAM. Korean content pairs with English math and code snippets for direct mappings to real-world libraries like OpenVINS. Users get instant access via a browser-based PDF viewer or downloadable 900-page slides, with Docker support to rebuild locally.

Why is it gaining traction?

It stands out by linking theory straight to production code—explaining why OpenVINS propagators or FAST-LIO2 IEKF lines are written that way, including quaternion conventions and Jacobian tricks devs debug for days. The structured progression from Bayes filters to LiDAR odometry skips fluff, arming users to read factor graphs and GTSAM factors without guessing. Pre-built online viewing and searchability make it dead simple to jump into specific lectures.

Who should use this?

SLAM grad students tackling their first ESKF or VIO implementation; robotics engineers bridging EKF basics to manifold estimation; researchers dissecting FAST-LIO2 or OpenVINS source for custom forks. Skip if you're new to SO(3) Lie groups or already hacking senior-level smoothers.

Verdict

Solid niche resource for estimation newcomers—docs shine with online access and build tools, but 16 stars and 1.0% credibility score signal early-stage maturity; test it if VIO code confuses you, otherwise hunt battle-tested alternatives. Worth starring for the code mappings alone.

(178 words)

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