SS-LCD

The core code for the paper Loop Closure Detection Based on Object-level Spatial Layout and Semantic Consistency, which has been submitted to TIM 2023.

Setup

1.1 Ubuntu and ROS

Ubuntu 18.04. ROS Melodic.

1.2 VisualDet3D

• environment setup

pip3 install -r requirement.txt

• configuration and path setup

Please modify the path and parameters in visualDet3D/config.py* files.

• stereo image as input training

./launchers/det_precompute.sh config/$CONFIG_FILE.py train
./launcher/train.sh  config/$CONFIG_FILE.py 0 $experiment_name

• stereo image as input testing

./launchers/det_precompute.sh config/$CONFIG_FILE.py test
./launchers/eval.sh config/$CONFIG_FILE.py 0 visualDet3D/workdirs/Stereo3D/checkpoint/Stereo3D_latest.pth test

• the evaluate results

the object detected results files should be stored in sequences/xx. VINS-Fusion could read the results.

1.3 VINS-Fusion

• environment setup

ceres solver: follow Ceres Installation

• an example

 roslaunch vins vins_rviz.launch
  rosrun vins kitti_odom_test ~/catkin_ws/src/VINS-Fusion/config/kitti_odom/kitti_config00-02.yaml YOUR_DATASET_FOLDER/sequences/00/

1.4 Spatial layout difference computing

With the object detection results and pose estimation results, you can test your own loop detection and store its results as two files, such as graph1.txt, graph2.txt.

The executable program will output the matched nodes with minimum distance between graph1 and graph2.

Notes

We are recently busy with review comments and revisions to the paper. The whole system for our paper will be released soon in this page.

Related Paper:

if you think the repository and codes are useful, please cite the following papers.

@ARTICLE{9327478,
  author={Y. {Liu} and Y. {Yuan} and M. {Liu}},
  journal={IEEE Robotics and Automation Letters}, 
  title={Ground-aware Monocular 3D Object Detection for Autonomous Driving}, 
  year={2021},
  doi={10.1109/LRA.2021.3052442}}
  
@article{qin2019fusion,
  author={Tong Qin and Jie Pan and Shaozu Cao and Shaojie Shen},
  title={A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors},
  journal={ArXiv},
  year={2019},
  volume={abs/1901.03638}}