ROG-Map

ROG-Map: An Efficient Robocentric Occupancy Grid Map for Large-scene and High-resolution LiDAR-based Motion Planning

Preprint: https://arxiv.org/abs/2302.14819

@article{ren2023rogmap,
    title={ROG-Map: An Efficient Robocentric Occupancy Grid Map for Large-scene and High-resolution LiDAR-based Motion Planning},
    author={Yunfan Ren and Yixi Cai and Fangcheng Zhu and Siqi Liang and Fu Zhang},
    journal={arXiv preprint arXiv:2302.14819},
    year={2023}
}

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1 About ROG-Map

1.1 What can ROG-Map do?

The ROG-Map is an occupancy grid map (OGM), and all methods based on OGM can be seamlessly implemented on ROG-Map, including:

• A* path search.
• Flight corridor generation.
• Frontier generation for autonomous exploration.
• Point collision check and line segment collision check.
• Box search.
• ...

We will provide numerous examples to help you apply ROG-Map to your own projects.

1.2 What are the differences compared to existing methods?

• Using a zero-copy map sliding strategy, ROG-Map maintains only a local map near the robot, enabling it to handle large-scale scene missions in unbounded environments.
• A novel incremental inflation method significantly decreases the computation time of obstacle inflation.

1.3 How can I test it?

When the code is released, you can test it with

1. Run with FAST-LIO: A computationally efficient and robust LiDAR-inertial odometry (LIO) package

   1. Building a robocentric occupancy grid map directly using FAST-LIO as input.

2. Run with MARSIM

   1. With MARSIM, you can test your own motion planning algorithms based on ROG-Map.

2 Date of code release

Our paper is currently under review, and the code of ROG-Map will be released as our work is accepted.