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Dinusharg/
TerrainFreeSpaceNet

autonomous-navigation

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TerrainFreeSpaceNet

TerrainFreeSpaceNet is a deep learning model designed to predict terrain free-space from 3D point cloud data.
It enables robots to estimate terrain traversability in uneven environments using raw point cloud observations.

This model is based on a PointNet-style neural network that processes unordered 3D points and outputs a normalized free-space score.

Overview

Autonomous ground robots operating in outdoor environments often encounter:

uneven terrain
vegetation
slopes and depressions
irregular obstacles

Traditional geometric free-space detection methods may struggle in these environments.

TerrainFreeSpaceNet learns to estimate terrain traversability directly from 3D point clouds, making it suitable for:

outdoor robotics
off-road navigation
agricultural robots
exploration robots

Model Architecture

The model uses a PointNet-style architecture consisting of:

Shared MLP layers implemented using Conv1D
Batch normalization and ReLU activation
Global max pooling
Fully connected regression layers
Sigmoid output for normalized free-space score

Input shape:

[B, 3, N]

Where:

B = batch size
3 = (x,y,z) coordinates
N = number of sampled points

Output:

[B,1]

Free-space score in range:

0 → non-traversable
1 → highly traversable

Input Format

The model expects CSV point cloud input.

Example:

x,y,z
0.12,0.31,0.02
0.15,0.34,0.05
0.18,0.29,0.04

Example Inference

Example usage using the provided inference script.

python

from inference import run_inference

score = run_inference("sample_input.csv", checkpoint="model.pt")

print("Free space score:", score)

Example output:

Free space score: 0.84

Training

The model was trained using frame-level 3D point cloud samples.

Each frame contains:

frame_id,x,y,z,free_space

Where:

frame_id identifies the point cloud frame
free_space represents terrain traversability score

Applications

TerrainFreeSpaceNet can be used for:

terrain-aware robot navigation
autonomous ground vehicles
off-road robotics
agricultural robots
exploration robots
rough terrain mobility analysis

Limitations

The model currently assumes XYZ coordinates only
Performance depends on training dataset diversity
Large terrain variations may require retraining
Real-time deployment requires optimized inference

Related Research

This model was developed as part of research on:

Autonomous robot navigation in uneven terrain using 3D perception.

It is designed to integrate with the Agoraphilic-3D Navigation Framework.

Repository

Full project code available here:

https://github.com/dinusharg/TerrainFreeSpaceNet

Citation

If you use this model in research, please cite:

bibtex

@article{10.1007/s12555-025-0624-2,
   author = {Gunathilaka, W. M. Dinusha and Kahandawa, Gayan and Ibrahim, M. Yousef and Hewawasam, H. S. and Nguyen, Linh},
   title = {Agoraphilic-3D Net: A Deep Learning Method for Attractive Force Estimation in Mapless Path Planning for Unstructured Terrain},
   journal = {International Journal of Control, Automation and Systems},
   volume = {23},
   number = {12},
   pages = {3790-3802},
   ISSN = {2005-4092},
   DOI = {10.1007/s12555-025-0624-2},
   url = {https://doi.org/10.1007/s12555-025-0624-2},
   year = {2025},
   type = {Journal Article}
}

Dinusharg/TerrainFreeSpaceNet