| | ---
|
| | license: apache-2.0
|
| | library_name: pytorch
|
| | tags:
|
| | - robotics
|
| | - point-cloud
|
| | - terrain-analysis
|
| | - autonomous-navigation
|
| | - 3d-perception
|
| | - deep-learning
|
| | - PointNet
|
| | ---
|
| |
|
| | # 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:
|
| |
|
| | 1. Shared MLP layers implemented using Conv1D
|
| | 2. Batch normalization and ReLU activation
|
| | 3. Global max pooling
|
| | 4. Fully connected regression layers
|
| | 5. 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}
|
| | }
|
| | |
