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https://github.com/gisbi-kim/PyICP-SLAM
Full-python LiDAR SLAM using ICP and Scan Context
https://github.com/gisbi-kim/PyICP-SLAM
icp lidar loop odometry place pointcloud recognition robot scan slam
Last synced: 3 months ago
JSON representation
Full-python LiDAR SLAM using ICP and Scan Context
- Host: GitHub
- URL: https://github.com/gisbi-kim/PyICP-SLAM
- Owner: gisbi-kim
- Created: 2019-10-06T13:21:30.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2023-11-17T10:15:32.000Z (about 1 year ago)
- Last Synced: 2024-08-02T07:07:31.084Z (6 months ago)
- Topics: icp, lidar, loop, odometry, place, pointcloud, recognition, robot, scan, slam
- Language: Python
- Size: 19.5 MB
- Stars: 486
- Watchers: 12
- Forks: 78
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- Awesome-SLAM - PyICP SLAM - python LiDAR SLAM using ICP and Scan Context (4. LIDAR based SLAM / 4.2 Others)
README
# PyICP SLAM
Full-python LiDAR SLAM.
- If you find a C++ version of this repo, go to [SC-LeGO-LOAM](https://github.com/irapkaist/SC-LeGO-LOAM) or [SC-A-LOAM](https://github.com/gisbi-kim/SC-A-LOAM).
### Purpose
- Full-python LiDAR SLAM
- Easy to exchange or connect with any Python-based components (e.g., DL front-ends such as [Deep Odometry](https://www.youtube.com/watch?v=Y2s08dv-Mq0))
- Here, ICP, which is a very basic option for LiDAR, and [Scan Context (IROS 18)](https://github.com/irapkaist/scancontext) are used for odometry and loop detection, respectively.
- Hands-on LiDAR SLAM
- Easy to understand (could be used for educational purpose)
- The practical use case of [miniSAM](https://github.com/dongjing3309/minisam)
- The miniSAM is easy to use at Python
### What is SLAM?
- In this repository, SLAM (Simultaneous localization and mapping) is considered as
- SLAM = Odometry + Loop closure
- In this repository, the state to be optimized only has robot poses; that is _pose-graph SLAM_.
### Overview of the PyICP SLAM
- The pipeline of the PyICP SLAM is composed of three parts
1. Odometry: [ICP (iterative closest point)](https://github.com/ClayFlannigan/icp/blob/master/icp.py)
- In here, Point-to-point and frame-to-frame (i.e., no local mapping)
2. Loop detection: [Scan Context (IROS 18)](https://github.com/irapkaist/scancontext)
- Reverse loop detection is supported.
3. Back-end (graph optimizer): [miniSAM](https://github.com/dongjing3309/minisam)
- Python API
### Features
- Thanks to the Scan Context, reverse loops can be successfully closed.
- E.g., see _KITTI 14_ at Results section below.
- Time costs
- (Here, no accelerated and naive) ICP gets 7-10 Hz for randomly downsampled points (7000 points)
- (Here, no accelerated and naive) Scan Context gets 1-2 Hz (when 10 ringkey candidates).
- if you need the more faster one, consider the C++ version of Scan Context (e.g., https://github.com/irapkaist/SC-LeGO-LOAM)
- miniSAM is enough fast.
### How to use
Just run
```sh
$ python3 main_icp_slam.py
```
The details of parameters are eaily found in the argparser in that .py file.
### Results (KITTI dataset)
Those results are produced under the same parameter conditions:
- ICP used random downsampling, 7000 points.
- Scan Context's parameters:
- Ring: 20, Sector: 60
- The number of ringkey candidates: 30
- Correct Loop threshold: 0.17 for 09, 0.15 for 14, and 0.11 for all others
Results (left to right):
- 00 (loop), 01, 02 (loop), 03
- 04, 05 (loop), 06 (loop), 09 (loop)
- 10, 11, 12, 13 (loop)
- 14 (loop), 15 (loop), 16 (loop), 17
- 18 (loop), 20
Some of the results are good, and some of them are not enough.
Those results are for the study to understand when is the algorithm works or not.
### Findings
- The Scan Context does not find loops well when there is a lane level change (i.e., KITTI 08, as below figures).
- If the loop threshold is too low (0.07 in the below figure), no loops are detected and thus the odometry errors cannot be reduced.
- 
- If the loop threshold is high (0.20 in the below figure), false loops are detected and thus the graph optimization failed.
- 
- but using this non-conservative threshold with a robust kernel would be a solution.
- see https://github.com/irapkaist/SC-LeGO-LOAM/blob/3170ba1de7bb0d3f8de5192742e0bbe267415ab4/SC-LeGO-LOAM/LeGO-LOAM/src/mapOptmization.cpp#L994
### Author
```sh
Giseop Kim ([email protected])
```
### Contirbutors
```sh
@JustWon
- Supports Pangolin-based point cloud visualization along the SLAM poses.
- Go to https://github.com/JustWon/PyICP-SLAM
```