https://github.com/KIT-MRT/pointcloud_surface

Ground surface estimation algorithms based on point clouds.
https://github.com/KIT-MRT/pointcloud_surface

fusion lidar point-cloud publication spline

Last synced: 3 months ago
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Ground surface estimation algorithms based on point clouds.

• Host: GitHub
• URL: https://github.com/KIT-MRT/pointcloud_surface
• Owner: KIT-MRT
• Created: 2021-08-30T08:29:09.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2022-03-03T12:11:08.000Z (almost 3 years ago)
• Last Synced: 2024-08-01T03:21:09.230Z (6 months ago)
• Topics: fusion, lidar, point-cloud, publication, spline
• Language: C++
• Homepage:
• Size: 6.43 MB
• Stars: 44
• Watchers: 7
• Forks: 4
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • Codeowners: CODEOWNERS

Awesome Lists containing this project

README

        

# Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines

This repository contains the source code, examples and further information regarding our [approach](http://arxiv.org/abs/2203.01180) of estimating a ground surface from LIDAR Measurements using Uniform B-Splines.

## Publication

**Titel**: Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines

**Authors**: Sascha Wirges and Kevin Rösch and Frank Bieder and Christoph Stiller

**Abstract**: We propose a fast and robust method to estimate the ground surface from LIDAR measurements on an automated vehicle. The ground surface is modeled as a UBS which is robust towards varying measurement densities and with a single parameter controlling the smoothness prior. We model the estimation process as a robust LS optimization problem which can be reformulated as a linear problem and thus solved efficiently. Using the SemanticKITTI data set, we conduct a quantitative evaluation by classifying the point-wise semantic annotations into ground and non-ground points. Finally, we validate the approach on our research vehicle in real-world scenarios.

**Citation**: If you use this source code, please cite its [paper](http://arxiv.org/abs/2203.01180)

```

@inproceedings{wirges2021groundsurfacce,

    title={Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines},   

    author={Sascha Wirges and Kevin Rösch and Frank Bieder and Christoph Stiller},

    year={2021},

    booktitle = {2021 IEEE 24th International Conference on Information Fusion (FUSION)},

}

```

## Usage

The repository is organized in the following format:

```bash

pointcloud_surface/

          ├── include/                  # header files

          ├── res/

                ├── assets/             # images for the github repo

                └── parameters.yaml     # parameter file

          ├── src/                      # source code including all cpp files

          └── test/                     # unit test for the ground surface estimation

```

### Dependencies

The dependencies can be reviewed in the file [package.xml](package.xml) and include catkin, mrt_cmake_modules, gtest, libgoogle-glog-dev, libceres-dev, libpcl-all-dev, uniform_bspline_ceres, uniform_bspline_eigen, util_ceres, util_eigen and util_yaml

### Run

tbd

## Results on our research vehicle

We implemented and validated our approach on our research vehicle [BerthaOne](https://ieeexplore.ieee.org/document/8055618)

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### Example of full 360° scans of all LIDARs on the experimental vehicle and estimated ground surface.

:



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### Example of our combined height map fusing the ground surface height and max observed reflexion height in each grid cell:



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