FGO-MOT

This repository is a core 3D MOT component within SLAMMOT. It performs continuous object tracking and trajectory estimation for robust online tracking and smooth object trajectories.

Highlights

• Factor-graph optimization: models motion priors, observation constraints, and marginalization as factors and solves in a unified optimization framework.

Common dependencies

• ROS 1
• PCL, Eigen
• Ceres-Solver

Quick Start

1. Prepare a catkin workspace and place this package in src/:

mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src
git clone https://github.com/shqfeng/LVIMOT.git
cd ..
catkin_make
source devel/setup.bash

2. Launch tracking (example):

roslaunch fgo-mot run.launch

Data and Configuration

• Datasets:

  • KITTI tracking pose data and object detections from AB3DMOT.

  Organize KITTI tracking data as:

  # KITTI Tracking Dataset
  └── kitti_tracking
         ├── testing 
         |      ├──calib
         |      |    ├──0000.txt
         |      |    ├──....txt
         |      |    └──0028.txt
         |      ├──image_02
         |      |    ├──0000
         |      |    ├──....
         |      |    └──0028
         |      ├──pose
         |      |    ├──0000
         |      |    |    └──pose.txt
         |      |    ├──....
         |      |    └──0028
         |      |         └──pose.txt
         |      ├──label_02
         |      |    ├──0000.txt
         |      |    ├──....txt
         |      |    └──0028.txt
         |      └──velodyne
         |           ├──0000
         |           ├──....
         |           └──0028      
         └── training  # same structure as testing
                ├──calib
                ├──image_02
                ├──pose
                ├──label_02
                └──velodyne 
  

  Example detections layout:

  └── point-rcnn
         ├── training
         |      ├──0000
         |      |    ├──000001.txt
         |      |    ├──....txt
         |      |    └──000153.txt
         |      ├──...
         |      └──0020
         └── testing
  

• Parameters:

  • tracking/optimization parameters: config/config.yaml.
  • If you maintain a custom tracker config (paths, thresholds), place it under config.

• Evaluation:

  • Results are saved to ~/catkin_ws/output/tracking_kitti/.
  • Evaluate using 3DMOT metrics AB3DMOT.

Citation

Please cite the following related works:

@article{feng2023fgomot,
       title   = {Accurate and Real-Time 3D-LiDAR Multi-Object Tracking Using Factor Graph Optimization},
       author  = {Feng, S. and Li, X. and Yan, Z. and others},
       journal = {IEEE Sensors Journal},
       year    = {2023}
}

@article{feng2025lvimot,
       title   = {LVIMOT: Accurate and robust LiDAR-visual-inertial localization and multi-object tracking in dynamic environments via tightly coupled integration},
       author  = {Feng, S. and Li, X. and Yan, Z. and others},
       journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
       year    = {2025},
       volume  = {230},
       pages   = {675--692}
}

@article{li2024liolot,
       title   = {LIO-LOT: Tightly-Coupled Multi-object Tracking and LiDAR-Inertial Odometry},
       author  = {Li, X. and Yan, Z. and Feng, S. and others},
       journal = {IEEE Transactions on Intelligent Transportation Systems},
       year    = {2024}
}

@article{feng2023vimot,
       title   = {VIMOT: A Tightly-Coupled Estimator for Stereo Visual-Inertial Navigation and Multi-Object Tracking},
       author  = {Feng, S. and Li, X. and Xia, C. and others},
       journal = {IEEE Transactions on Instrumentation and Measurement},
       year    = {2023}
}

@article{feng2024LVMOT,
       title   = {Tightly Coupled Integration of LiDAR and Vision for 3D Multiobject Tracking},
       author  = {Feng, S. and Li, X. and Yan, Z. and others},
       journal = {IEEE Transactions on Intelligent Vehicles},
       year    = {2024}
}