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I-RIKF-LIO

★ 51 Open GitHub ↗

implementation of lidar-inertial odometry based on Iterated Right-Invariant Kalman Filter

// repository documentation

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# I-RIKF-LIO

**Personal learning project** — similar to Fast-LIO2, based on Eigen library, implementing **lidar-inertial odometry** using:

- Iterated Right-Invariant Kalman Filter (IRIKF) — **removed** due to unstable implementation  
- Iterated Error State Kalman Filter (IESKF, under development)

> ⚠️ **Warning:** Bad coding skills. This project is messy. Use with caution.

---

## Comparison Result

| Faster LIO | I-RIKF |
|------------|--------|
| ![Faster LIO](https://raw.githubusercontent.com/wxpiggy/I-RIKF-LIO/master/images/fasterlio.png) | ![I-RIKF](https://raw.githubusercontent.com/wxpiggy/I-RIKF-LIO/master/images/ours.png) |

**Map:**  
![Map](https://raw.githubusercontent.com/wxpiggy/I-RIKF-LIO/master/images/map.png)

**Pangolin Viewer:**  
![Pangolin](https://raw.githubusercontent.com/wxpiggy/I-RIKF-LIO/master/images/pangolin.png)

---

## TODO

- [x] Add `time_unit` adaptation for Velodyne  
- [x] Save trajectory and map  
- [x] Implement iterated version of Right-Invariant Kalman Filter  
- [x] Add Pangolin support, remove RViz (RViz optional)  
- [x] Add IKdtree for improved real-time performance (some code cleaning pending)  
- [x] Add loop closure to improve global consistency (branch `dev/loop`)  
- [ ] Add extrinsic estimation (probably not doing this)

---

## Acknowledgments

A big thanks to [mengkai98/ieskf_slam](https://github.com/mengkai98/ieskf_slam) for the detailed tutorial and excellent code — this project is built based on his work.

I-RIKF-LIO

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