PFilter

• This work is the official implementation of "PFilter: Building Persistent Maps through Feature Filtering for Fast and Accurate LiDAR-based SLAM", which saves 20.9% processing time per frame and improves the accuracy by 9.4% than FLOAM.

• This code is modified from FLOAM.

[Demo Video] [Preprint Paper]

Modifier: Yifan Duan, University of Science and Technology of China, China

1. Demo

• Edge map and surface map on KITTI dataset:

• Map in USTC with 32-lines LiDAR:

2. Prerequisites and build

• It's similar to FLOAM, please see FLOAM for the build details.

3. Usage

• To test on KITTI dataset and get the same ATEs as the shown in the Sec. 4:

  • roslaunch pfilter pfilter_kitti.launch bag_filename:=/YOUR/BAG/PATH

• To test on other data:

  • roslaunch pfilter pfilter.launch bag_filename:=/YOUR/BAG/PATH k_new:=xx theta_p:=xx theta_max:=xx topic:=xx
  • The xx should be replaced with suitable value.
    • "k_new " is Int $\in [0,\infty)$, but [0,5] is recommanded.
    • "theta_p " is Float $\in [0,\infty)$, but [0,3] is recommanded.
    • "theta_max " is Int $\in [0,255]$.
    • "topic" is the ros topic of LIDAR points.

• Some notes:

  • The parameters "k_new", "theta_p" and "theta_max" can control how hard PFilter works, which means how many noise points will be removed from the map.
  • k_new $\downarrow$, theta_p $\uparrow$, theta_max $\uparrow$ means more points are removed.
  • Generally speaking, if the robot is moving slow, I usually let PFilter remove more points to keep the map tiny. For example, I use a low-speed unmanned vehicle on campus, equipped with a 32-lines LiDAR for mapping, and the parameters I use are "0, 1, 200". In comparison, the parameters used on KITTI dataset are "0, 0.4, 75".
  • To get the performance of origin FLOAM, you can set the parameters to "0,0,0".

4. Evluation

KITTI sequence	FLOAM	PFilter	KITTI sequence	FLOAM	PFilter
00	0.7007%	0.6208%	06	0.5435%	0.4906%
01	1.9504%	1.8055%	07	0.4159%	0.3740%
02	0.9549%	0.8042%	08	0.9349%	0.9326%
03	0.9549%	0.8941%	09	0.7031%	0.6242%
04	0.6875%	0.6420%	10	1.0257%	0.9206%
05	0.4910%	0.5085%	all	0.8511%	0.7833%

• The tool for evaluation is KITTI odometry evaluation tool.

5.Acknowledgements

Thanks for A-LOAM and F-LOAM.

6. Citation

If you use this work for your research, you may want to cite

@inproceedings{duan2022pfilter,
  title={PFilter: Building Persistent Maps through Feature Filtering for Fast and Accurate LiDAR-based SLAM},
  author={Duan, Yifan and Peng, Jie and Zhang, Yu and Ji, Jianmin and Zhang, Yanyong},
  booktitle={2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={11087--11093},
  year={2022},
  organization={IEEE}
}