# Ground Profile Recovery from Aerial 3D LiDAR-based Maps

## TL;DR

This paper develops a method for extracting ground surfaces from aerial LiDAR point clouds in forestry regions, using Cloth Simulation Filtering, demonstrated through drone flights over varied terrain.

## Contribution

It introduces a ground detection approach with filtering of forest points using CSF, enabling accurate landscape mapping from aerial LiDAR data.

## Key findings

- Effective ground detection in forestry regions
- Robustness demonstrated in outdoor drone experiments
- Successful terrain recovery with LiDAR data

## Abstract

The paper presents the study and implementation of the ground detection methodology with filtration and removal of forest points from LiDAR-based 3D point cloud using the Cloth Simulation Filtering (CSF) algorithm. The methodology allows to recover a terrestrial relief and create a landscape map of a forestry region. As the proof-of-concept, we provided the outdoor flight experiment, launching a hexacopter under a mixed forestry region with sharp ground changes nearby Innopolis city (Russia), which demonstrated the encouraging results for both ground detection and methodology robustness.