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Optimized-SC-F-LOAM#

What is Optimized-SC-F-LOAM?#

  • An improved version of F-LOAM and uses an adaptive threshold to further judge the loop closure detection results and reducing false loop closure detections. Also it uses feature point-based matching to calculate the constraints between a pair of loop closure frame point clouds and decreases time consumption of constructing loop frame constraints.

Repository Information#

https://github.com/SlamCabbage/Optimized-SC-F-LOAM

Required Sensors#

  • LIDAR [VLP-16, HDL-32, HDL-64]

ROS Compatibility#

  • ROS 1

Dependencies#

  • ROS
  • PCL
  • GTSAM
  • Ceres Solver
  • For visualization purpose, this package uses hector trajectory sever, you may install the package by
sudo apt-get install ros-noetic-hector-trajectory-server

Build & Run#

1) Build#

cd ~/catkin_ws/src
git clone https://github.com/SlamCabbage/Optimized-SC-F-LOAM.git
cd ..
catkin_make

2) Create message file#

In this folder, Ground Truth information, optimized pose information, F-LOAM pose information and time information are stored

mkdir -p ~/message/Scans

Change line 383 in the laserLoopOptimizationNode.cpp to your own "message" folder path

(Do not forget to rebuild your package)

3) Set parameters#

  • Set LIDAR topic and LIDAR properties on 'sc_f_loam_mapping.launch'

4) Run#

source devel/setup.bash
roslaunch optimized_sc_f_loam optimized_sc_f_loam_mapping.launch

Example Result#

image

Results on KITTI Sequence 00 and Sequence 05#

image

Comparison of trajectories on KITTI dataset#

image

Test on KITTI sequence You can download the sequence 00 and 05 datasets from the KITTI official website and convert them into bag files using the kitti2bag open source method.

00: 2011_10_03_drive_0027 000000 004540

05: 2011_09_30_drive_0018 000000 002760

See the link: https://github.com/ethz-asl/kitti_to_rosbag

Acknowledgements#

Thanks for SC-A-LOAM(Scan context: Egocentric spatial descriptor for place recognition within 3d point cloud map) and F-LOAM(F-LOAM : Fast LiDAR Odometry and Mapping).

Citation#

@misc{https://doi.org/10.48550/arxiv.2204.04932,
  doi = {10.48550/ARXIV.2204.04932},

  url = {https://arxiv.org/abs/2204.04932},

  author = {Liao, Lizhou and Fu, Chunyun and Feng, Binbin and Su, Tian},

  keywords = {Robotics (cs.RO), FOS: Computer and information sciences, FOS: Computer and information sciences},

  title = {Optimized SC-F-LOAM: Optimized Fast LiDAR Odometry and Mapping Using Scan Context},

  publisher = {arXiv},

  year = {2022},

  copyright = {arXiv.org perpetual, non-exclusive license}
}