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https://github.com/gisbi-kim/SC-A-LOAM
Robust LiDAR SLAM with a versatile plug-and-play loop closing and pose-graph optimization.
https://github.com/gisbi-kim/SC-A-LOAM
gps gtsam lidar lidar-mapping lidar-slam livox-lidar loam localization mapping mulran-dataset odometry place-recognition point-cloud scancontext slam
Last synced: 4 months ago
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Robust LiDAR SLAM with a versatile plug-and-play loop closing and pose-graph optimization.
- Host: GitHub
- URL: https://github.com/gisbi-kim/SC-A-LOAM
- Owner: gisbi-kim
- Created: 2021-05-10T12:29:05.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2023-01-30T17:47:02.000Z (over 1 year ago)
- Last Synced: 2023-11-24T02:45:31.432Z (7 months ago)
- Topics: gps, gtsam, lidar, lidar-mapping, lidar-slam, livox-lidar, loam, localization, mapping, mulran-dataset, odometry, place-recognition, point-cloud, scancontext, slam
- Language: C++
- Homepage:
- Size: 45 MB
- Stars: 355
- Watchers: 12
- Forks: 92
- Open Issues: 17
-
Metadata Files:
- Readme: README.md
Lists
- Awesome-Autonomous-Driving - SC-A-LOAM - Scancontext + 现在的SOTA里程计(Lego-loam, fast lio, a loam etc.) (5 Localization and SLAM / 3.4 High Performance Inference)
README
# SC-A-LOAM
## News
- ``2021-07-16``: This repository's easy-to-use plug-and-play loop detection and pose graph optimization module (named [SC-PGO](https://github.com/gisbi-kim/SC-A-LOAM/blob/main/src/laserPosegraphOptimization.cpp)) is also integrated with FAST-LIO2! see [FAST_LIO_SLAM](https://github.com/gisbi-kim/FAST_LIO_SLAM).
## What is SC-A-LOAM?
- A real-time LiDAR SLAM package that integrates A-LOAM and ScanContext.
- **A-LOAM** for odometry (i.e., consecutive motion estimation)
- **ScanContext** for coarse global localization that can deal with big drifts (i.e., place recognition as kidnapped robot problem without initial pose)
- and iSAM2 of GTSAM is used for pose-graph optimization.
- This package aims to show ScanContext's handy applicability.
- The only things a user should do is just to include `Scancontext.h`, call `makeAndSaveScancontextAndKeys` and `detectLoopClosureID`.
## Features
1. A strong place recognition and loop closing
- We integrated ScanContext as a loop detector into A-LOAM, and ISAM2-based pose-graph optimization is followed. (see https://youtu.be/okML_zNadhY?t=313 to enjoy the drift-closing moment)
2. A modular implementation
- The only difference from A-LOAM is the addition of the `laserPosegraphOptimization.cpp` file. In the new file, we subscribe the point cloud topic and odometry topic (as a result of A-LOAM, published from `laserMapping.cpp`). That is, our implementation is generic to any front-end odometry methods. Thus, our pose-graph optimization module (i.e., `laserPosegraphOptimization.cpp`) can easily be integrated with any odometry algorithms such as non-LOAM family or even other sensors (e.g., visual odometry).
-
3. (optional) Altitude stabilization using consumer-level GPS
- To make a result more trustworthy, we supports GPS (consumer-level price, such as U-Blox EVK-7P)-based altitude stabilization. The LOAM family of methods are known to be susceptible to z-errors in outdoors. We used the robust loss for only the altitude term. For the details, see the variable `robustGPSNoise` in the `laserPosegraphOptimization.cpp` file.
## Prerequisites (dependencies)
- We mainly depend on ROS, Ceres (for A-LOAM), and GTSAM (for pose-graph optimization).
- For the details to install the prerequisites, please follow the A-LOAM and LIO-SAM repositiory.
- The below examples are done under ROS melodic (ubuntu 18) and GTSAM version 4.x.
## How to use?
- First, install the abovementioned dependencies, and follow below lines.
```
mkdir -p ~/catkin_scaloam_ws/src
cd ~/catkin_scaloam_ws/src
git clone https://github.com/gisbi-kim/SC-A-LOAM.git
cd ../
catkin_make
source ~/catkin_scaloam_ws/devel/setup.bash
roslaunch aloam_velodyne aloam_mulran.launch # for MulRan dataset setting
```
## Example Results
### Riverside 01, MulRan dataset
- The MulRan dataset provides lidar scans (Ouster OS1-64, horizontally mounted, 10Hz) and consumer level gps (U-Blox EVK-7P, 4Hz) data.
- About how to use (publishing data) data: see here https://github.com/irapkaist/file_player_mulran
- example videos on Riverside 01 sequence.
1. with consumer level GPS-based altitude stabilization: https://youtu.be/FwAVX5TVm04
2. without the z stabilization: https://youtu.be/okML_zNadhY
- example result:
### KITTI 05
- For KITTI (HDL-64 sensor), run using the command
```
roslaunch aloam_velodyne aloam_velodyne_HDL_64.launch # for KITTI dataset setting
```
- To publish KITTI scans, you can use mini-kitti publisher, a simple python script: https://github.com/gisbi-kim/mini-kitti-publisher
- example video (no GPS used here): https://youtu.be/hk3Xx8SKkv4
- example result:
### Indoor
- ScanContext also works at indoor environments (use smaller sc_max_radius value).
- example video: https://youtu.be/Uv6_BRmxJho
- example result:
### For Livox LiDAR
- Scan Context also works for Livox LiDAR data
- In this example, Scan Context is integrated with FAST-LIO (https://github.com/hku-mars/FAST_LIO).
- Note: No additional integration effort is required. A user just run seperately FAST-LIO node and SC-A-LOAM's posegraphoptimization.cpp node!
- example video (tutoial and results): https://youtu.be/Fw9S6D6HozA
- example result:
### For Navtech Radar
- Scan Context also works for Navtech Radar data!
- For the details, please see
- https://github.com/gisbi-kim/navtech-radar-slam
- used the pose-graph optimization node of this repository (SC-A-LOAM)
- [example video](https://www.youtube.com/watch?v=avtIQ8fesgU&t=128s)
## Utilities
### Data saver and Map construction
- Similar to the [SC-LIO-SAM's saver utility](https://github.com/gisbi-kim/SC-LIO-SAM#applications), we support pose and scan saver per keyframes. Using these saved data, the map (within ROI) can be constructed offline. See the `utils/python/makeMergedMap.py` and [this tutorial](https://youtu.be/jmR3DH_A4Co).
- Below is the example results of MulRan dataset KAIST 03's merged map, visualized using CloudCompare ([download the map data here](https://www.dropbox.com/sh/96jrpx3x6hh316j/AACb07kGbocnQWMIpksmU6MQa?dl=0)).
- A user also can remove dynamic points using these saved keyframe poses and scans. See [this tutorial](https://www.youtube.com/watch?v=UiYYrPMcIRU) and our [Removert project](https://github.com/irapkaist/removert).
## Acknowledgements
- Thanks to LOAM, A-LOAM, and LIO-SAM code authors. The major codes in this repository are borrowed from their efforts.
## Maintainer
- please contact me through `[email protected]`
## TODO
- Delayed RS loop closings
- SLAM with multi-session localization
- More examples on other datasets (KITTI, complex urban dataset, etc.)