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Real-time Radar SLAM: ORORA + ScanContext
| Date | Stars |
|---|---|
| 2026-07-24 | 266 |
| 2026-07-25 | 266 |
| 2026-07-28 | 266 |
| 2026-07-30 | 266 |
| 2026-08-06 | 266 |
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<h1>Navtech-Radar-SLAM</h1>
<a href="https://github.com/gisbi-kim/navtech-radar-slam/tree/main"><img src="https://img.shields.io/badge/ROS-Noetic-blue" /></a>
<a href="https://github.com/gisbi-kim/navtech-radar-slam/tree/main"><img src="https://img.shields.io/badge/Linux-FCC624?logo=linux&logoColor=black" /></a>
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<a href=https://www.youtube.com/watch?v=7ZMPtornIHA>Video</a>
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<a href="https://github.com/gisbi-kim/navtech-radar-slam/tree/main?tab=readme-ov-file#steps">Install</a>
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<a href=https://arxiv.org/abs/2303.01876>Paper (ORORA)</a>
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<a href=https://ieeexplore.ieee.org/abstract/document/8593953>Paper (ScanContext)</a>
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<p align="center"><img src=pic/ORORA_SLAM_GIF-ezgif.com-optimize-resized.gif alt="animated" /></p>
</div>
**ORORA-SLAM**: SLAM using [Outlier-robust radar odometry (ORORA)](https://arxiv.org/abs/2303.01876) + [ScanContext-based Loop Closing](https://ieeexplore.ieee.org/abstract/document/8593953)
---
## What is Navtech-Radar-SLAM?
- In this repository, a (minimal) SLAM problem is defeind as **SLAM = Odometry + Loop closing**, and the optimized states are only robot poses along a trajectory.
- Based on the above view, this repository aims to integrate current available radar odometry, radar place recognition, and pose-graph optimization.
1. Radar odometry: [ORORA open source](https://github.com/url-kaist/outlier-robust-radar-odometry).
- The odometry modules consumes file-based input (not ROS subscription) in this example. See [here](https://github.com/url-kaist/outlier-robust-radar-odometry/blob/main/src/odometry.cpp) for the details.
2. Radar place recognition: [Scan Context open source](https://github.com/irapkaist/scancontext)
- In MulRan dataset paper, the radar scan context is also proposed, but in this repository we use a Cartesian 2D feature point cloud (extracted via cen2019 method) as an input for the original Scan Context (IROS2018) method and it works.
- The Scan Context-based loop detection is included in the file `pgo/SC-A-LOAM/laserPosegraphOptimization.cpp`.
3. Pose-graph optimization
- iSAM2 in GTSAM is used. See `pgo/SC-A-LOAM/laserPosegraphOptimization.cpp` for the details (ps. the implementation is eqaul to [SC-A-LOAM](https://github.com/gisbi-kim/SC-A-LOAM) and it means `laserPosegraphOptimization.cpp` node is generic!)
---
## How to use?
### Dependencies
- ORORA: OpenCV, and SC-PGO: GTSAM
- Code is tested on Ubuntu 20.04 with ROS Noetic.
### Steps
First, clone and build. Note, there's a **submodule** in the repository.
```
$ mkdir -p ~/catkin_radarslam/src && cd ~/catkin_radarslam/src
$ git clone https://github.com/gisbi-kim/navtech-radar-slam.git
$ cd navtech-radar-slam && git submodule init && git submodule update
$ cd ../..
$ catkin_make
```
Second,
- [Download a sequence from the MulRan dataset](https://sites.google.com/view/mulran-pr/download) (you need to download polar_oxford_form.zip)
- Change [this line in the yeti launch](https://github.com/gisbi-kim/navtech-radar-slam/blob/2921851c0735894d38998ca1a2057af2e8abbfd9/odometry/yeti_radar_odometry/launch/yeti_radar_odometry.launch#L4) to your downloaded and unzipped radar data directory path.
Then, enjoy!
```
$ source devel/setup.bash
$ roslaunch src/navtech-radar-slam/launch/navtech_radar_slam_mulran.launch seq_dir:=${DATA_DIR}
```
For example,
```
$ roslaunch src/navtech-radar-slam/launch/navtech_radar_slam_mulran.launch seq_dir:="/media/shapelim/UX980/UX960NVMe/mulran-radar/KAIST03"
```
## Examples
- The examples are from [MulRan dataset](https://sites.google.com/view/mulran-pr/home), which is suitable to evaluate the radar odometry or SLAM algorithm in complex urban sites.
- The MulRan dataset provides the oxford-radar-robotcar-radaExcerpt of 7,022 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3c6b7c5d0540fb5b, topic:slam, name:slam, desc:slam