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Terra: Hierarchical Terrain-Aware 3D Scene Graph for Task-Agnostic Outdoor Mapping

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# Terra: Hierarchical Terrain-Aware 3D Scene Graph for Task-Agnostic Outdoor Mapping
This repository contains the code for *Terra: Hierarchical Terrain-Aware 3D Scene Graph for Task-Agnostic Outdoor Mapping*.
Terra is an open-source 3D Scene Graph (3DSG) framework developed by the BYU FROST Lab for robotics. It enables robots to build semantic, hierarchical, and persistent scene representations for navigation, mapping, planning, and long-term autonomy.

![Terra](https://raw.githubusercontent.com/BYU-FROST-Lab/Terra/main/assets/method_fig.png)

# Table of Contents
- [Terra](#terra)
- [Table of Contents](#table-of-contents)
- [Paper](#paper)
- [Setup](#setup)
    - [Requirements](#requirements)
    - [Using Docker Container](#using-docker-container)
- [Datasets and Metric Mapping](#datasets-and-metric-mapping)
- [Building Terra](#building-terra)
- [Task-Execution with Terra](#task-execution-with-terra)

# Paper

**Preprint Citation:**

> C. R. Samuelson, A. Austin, S. Knoop, B. Romrell, G. R. Slade, T. W. McLain, and J. G. Mangelson, “Terra: Hierarchical Terrain-Aware 3D Scene Graph for Task-Agnostic Outdoor Mapping,” Sept 2025. [Online]. Available: https://arxiv.org/abs/2509.19579

**Accepted to ICRA 2026. Citation Pending.**

# Setup

### Requirements
* [Docker](https://docs.docker.com/engine/install/ubuntu/)
* NVIDIA GPU

Our repository is designed for ROS 2 Humble. To handle dependency issues, our repository is built in Docker using the following container.

### Using Docker Container

First pull our Terra docker container
```bash
docker pull frostlab/ros2_terra:latest
xhost +local:docker
```

Start the container in your terminal
```bash
docker run --rm -it --gpus all --net host -v /tmp/.X11-unix:/tmp/.X11-unix -v <path/to/terra_repo>:/docker_ros2_ws/src/terra -v <ros2_bags_folder>:/<ros2_bags_folder> -e DISPLAY=$DISPLAY frostlab/ros2_terra
```
Build and source the ros2 repository
```
tmux

cd docker_ros2_ws

colcon build --symlink-install --cmake-args -DCMAKE_BUILD_TYPE=Release

source install/setup.bash
```

# Datasets and Metric Mapping

The ros launch code uses LIO-SAM to build the metric map, extracts and saves relevant data in the dataset folder structure below.

<summary>Dataset folder structure</summary>

```
my_dataset
├── global_pc
|   ├── global_pc_{timestamp}.npy
|   ├── ...
├── camera1_images
|   ├── cam1_img_{timestamp}.jpg
|   ├── ...
├── lidar_pc
|   ├── lidar_pc_{timestamp}.npy
|   ├── ...
├── transformations_lidar2cam1
|   ├── transform_lidar_to_cam1_{timestamp}.npy
|   ├── ...
├── transformations_lidar2global
|   ├── transform_lidar_to_map_{timestamp}.npy
|   └── ...
```

</details>

<summary>Simulation: Business Campus Dataset</summary>

- Download the dataset: [Dense 1/8](https://gofile.me/7dj2d/g3y3vblhM) (3.30 GB), [Sparse 1/3](https://gofile.me/7dj2d/aJN8D565b) (1.66 GB), and [Sparse Full](https://gofile.me/7dj2d/HZqnxpZHR) (13.34 GB).
 - These links enable downloading the extracted metric data from each rosbag collected in HoloOcean.
 - For more information about the Business Campus World in HoloOcean see [here](https://byu-holoocean.github.io/holoocean-docs/v2.3.0/packages/BusinessCampus/BusinessCampus.html).
 - For more information on installing this world in HoloOcean see [here](https://byu-holoocean.github.io/holoocean-docs/v2.3.0/packages/docs/installation.html).
 - For more information on runing ros2 in HoloOcean see [here](https://byu-holoocean.github.io/holoocean-docs/v2.3.0/usage/ROS2.html).
 - Can extract subfolders into a single dataset folder with: `unzip holoocean_<dataset-name>.zip -d holoocean_<dataset-name>`
 - Make sure the location of this downloaded metric data has been volumed into the docker container.
- Download the simulated YOLO model [here](https://gofile.me/7dj2d/0wFLzTul8).
 - After unzipping, the model is located in: `holo_3cls_nano_stepsz25_256imgsz_500epochs/weights/best.pt`.
 - Identify your container name in a terminal with: `docker ps`
 - Copy the model into the container with:
 ```
 docker cp <yolo_folder_location>/holo_3cls_nano_stepsz25_256imgsz_500epochs/weights/best.pt <container_name>:/holoocean_3cls.pt
 ```
- Given this metric data, proceed to [Building Terra](#building-terra).

</details>

<summary>Real-World: South Campus Dataset</summary>

- Download the dataset (*in progress*) 
- Put the rosbag in the volumed `ros2_bags_folder` so you can access it in the docker container 
- Copy the contents of the `config/south_campus/liosam_params.yaml` file to replace the contents in the `params.yaml` file in the `LIO-SAM/config` folder
- Update the `config/south_campus/params.yaml` to match the correct `rosbag_path` and `save_folder`.
- Now to build the metric point cloud map with LIO-SAM and save the data into our folder structure, run
```bash
ros2 launch terra_ros build_metric_map_south_campus.launch.py
```
</details>

<summary>Custom Datasets</summary>

If you have a ROS 2 bag of your Ouster OS1-128 LiDAR and RGB Camera data, then do the following to run LIO-SAM:

- Edit the `params.yaml` file in the `LIO-SAM/config` folder to match your LiDAR and IMU ros topics as well as the extrinsic transformation between the two.
- Copy the contents `config/rviz2.rviz` file to replace the `rviz2.rviz` file in the `LIO-SAM/config` folder.
- Update the `params.yaml` ros parameters in `terra_ros/config` for your dataset and rosbag
- Now to build the metric point cloud map with LIO-SAM and save the data into our folder structure, run
```bash
ros2 launch terra_ros build_metric_map_multicam_rate.launch.py params_file:=<path_to_updated_params_file>.yaml
```
</details>

# Building Terra

Change into the Terra repo in container with: `cd /docker_ros2_ws/src/terra`

<summary>Building Metric-Semantic Map (MS Map)</summary>

Provided that you have all the data saved in the file structure shown above, you can build the metric-semantic map (ms map) as follows:
- Update the `terra/config/<dataset_name>/msmap.yaml` arguments to match the saved data folder path and YOLO terrain model location.
 - We provide different msmap yaml files showing capabilities of handling more than 1 camera image data as well as using YOLOE for datasets where you don't have a trained YOLO terrain model. 
- Run the MS Map code with the correct yaml filepath as an argument as follows: 
```bash
python3 -m terra.ms_map --params=terra/config/<dataset_name>/msmap.yaml
```
> Note: This will take a while to process at about 1 second per lidar-image pair.

To visualize the resulting MS Map, we have provided a helper script where you just need to pass in the filepath to your saved data folder. Each different semantic CLIP id will have a different color. For example:
```bash
python3 -m terra.visualize_msmap --data_folder=/data/folder --output_folder=/directory/to/ms_map/output --num_terrain=3 --pt_size=2.0
```

</details>

<summary>Building Region and Place Layers from MS Map</summary>

With the results saved from MS Map, you can build the terrain-aware places and region layers of the 3DSG as follows:
- Update the `terra/config/<dataset_name>/build_terra.yaml` arguments to match the saved MS Map files location and other relevant arguments.
- Run Build Terra code with your updated yaml file as an argument as follows: 
```bash
python3 -m terra.build_terra --params=/path/to/build_terra.yaml
```

To visualize the resulting 3D Scene Graph, run: 
```bash
python3 -m terra.visualize_terra --terra_3dsg=/path/to/saved/terra_nxgraph.pkl --global_pc=/folder/path/to/global_pc/ --num_terrains=num_terrains --view_json=view.json
```
- The arguments are defined as:
    - `terra_3dsg`: Path to the saved 3DSG. 
        - *Note: `build_terra.py` saves a terra_nxgraph.pkl and a Terra.pkl file. The first is just the nx.Graph 3DSG object and the latter is our Terra 3DSG class. This is asking for the first one.* 
    - `global_pc`: Path to the folder that contains all of the global point clouds saved from the metric mapping step.
    - `num_terrains`: Integer of the number of terrains used by the YOLO model
    - `view_json`: (Optional) Saved json file to tell Open3D to display the 3DSG at a certain angle and distance.

</details>

# Task-Execution with Terra

<summary>Object Retrieval Tasks</summary>

To perform object retrieval tasks with the Terra 3DSG saved from [Building Terra](#building-terra), is done as follows:
- Modify the `object_retrieval.yaml` file for as many object retrieval tasks of interest as well as other parameters described below.
- Run the object retrieval task as follows:
```bash
python3 -m terra.object_retrieval_task --params=/path/to/object_retrieval.yaml
```
- YAML parameters are defined as:
    - `terra`: Path to the saved Terra 3DSG. 
        - *Note: `build_terra.py` saves a terra_3dsg.pkl and a Terra.pkl file. The first is just the nx.Graph 3DSG object and the latter is our Terra 3DSG class. This is asking for the latter.*
    - `object_tasks`: YAML list of object tasks
    - `prediction_method`: Pass a string of the method to use from the following [ms_avg, ms_max, 3dsg]. These methods are explained in detail in the paper. (Default: `ms_avg`)
    - `alpha`: Threshold to determine whether an object is task relevant (i.e. if its cosine-similarity score is above `alpha` then it is task-relevant). (Default: `0.23`)

</details>

<summary>Region Monitoring Tasks</summary>

To perform region monitoring tasks with the Terra 3DSG saved from [Building Terra](#building-terra), is done as follows:
- Modify the `region_querying.yaml` file for as many region monitoring tasks of interest as well as other parameters described below.
- Run the region monitoring task as follows:
```bash
python3 -m terra.region_querying_task --params=/path/to/region_querying.yaml
```
- YAML parameters are defined as:
    - `terra`: Path to the saved Terra 3DSG. 
        - *Note: `build_terra.py` saves a terra_3dsg.pkl and a Terra.pkl file. The first is just the nx.Graph 3DSG object and the latter is our Terra 3DSG class. This is asking for the latter.*
    - `region_tasks`: YAML list of region monitoring tasks
    - `prediction_method`: Pass a string of the method to use from the following [max, thresh, mix, aib]. These methods are explained in detail in the paper. (Default: `max`)
    - `alpha`: Threshold to determine whether a region is task relevant (i.e. if its cosine-similarity score is above `alpha` then it is task-relevant). Only used for methods [thresh, mix, aib]. (Default: `0.23`) 
    - `k`: Parameter for selecting the top-K task relevant regions. Only used for methods [max, mix]. (Default: `1`)

</details>

<summary>Terrain-Aware Path Planning to Destination Query</summary>

To perform path planning to a destination query, do the following:
- Modify the `path_panning.yaml` file based on your destination query and the terrain preferences
- Run the path planning task as follows:
```bash
python3 -m terra.path_planning_task --params=/path/to/path_planning.yaml
```
- YAML parameters are defined as;
    - `terra`: Path to the saved Terra 3DSG. 
        - *Note: `build_terra.py` saves a terra_3dsg.pkl and a Terra.pkl file. The first is just the nx.Graph 3DSG object and the latter is our Terra 3DSG class. This is asking for the latter.*
    - `terrain_preferences`: List the terrain indices that are `preferred` and `forbidden` based on the terrain input and ordering used in `build_terra.yaml`. The `penalties` are defined by each terrain index and an associated weight. 
    - `queries`: 
        - `destination`: String explaining the destination query
        - `start`: (Optional) string explaining start location. If not used, default's to start being the first place node in the graph
    - `prediction_method`: Pass a string of the method to use from the following [ms_avg, ms_max, 3dsg_avg, 3dsg_max, ms_medoid, ms_trim]. These methods are explained in detail in the paper. (Default: `ms_avg`)
    - `alpha`: Threshold to determine whether an object is task relevant (i.e. if its cosine-similarity score is above `alpha` then it is task-relevant). (Default: `0.23`)

</details>

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