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https://github.com/ethz-asl/ssc_exploration
Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning
https://github.com/ethz-asl/ssc_exploration
3d completion exploration mapping mav planning robotics sc-explorer scene ssc
Last synced: 3 months ago
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Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning
- Host: GitHub
- URL: https://github.com/ethz-asl/ssc_exploration
- Owner: ethz-asl
- License: bsd-3-clause
- Created: 2022-08-17T08:19:56.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-06-06T13:12:37.000Z (8 months ago)
- Last Synced: 2024-06-06T14:46:25.812Z (8 months ago)
- Topics: 3d, completion, exploration, mapping, mav, planning, robotics, sc-explorer, scene, ssc
- Language: C++
- Homepage:
- Size: 937 KB
- Stars: 61
- Watchers: 20
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SSC Exploration
This repository contains the code for **SC-Explorer**, our framework for incremental fusion of 3D semantic scene completion and safe and efficient integration thereof into mobile robotic mapping and planning for exploration.
Incremental 3D scene completion for safe and efficient exploration mapping and planning.
# Table of Contents
**Credits**
* [Paper](#Paper)
* [Video](#Video)
**Setup**
* [Installation](#installation)
* [Simulation](#Simulation)
**Examples**
- [Training the Model](#training-the-model)
- [Running the Planner](#running-the-planner)
- [Evaluating an experiment](#evaluating-an-experiment)
# Paper
If you find this useful for your research, please consider citing our paper:
* Lukas Schmid, Mansoor Nasir Cheema, Victor Reijgwart, Roland Siegwart, Federico Tombari, and Cesar Cadena, "**SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning**" in *ArXiv Preprint*, 2022.
\[ [ArXiv](https://arxiv.org/abs/2208.08307) | [Video](https://youtu.be/DMXdhCqUqts)\]
```bibtex
@article{schmid2022scexplorer,
title={SC-Explorer: Incremental 3D Scene Completion for Safe and Efficient Exploration Mapping and Planning},
author={Schmid, Lukas and Cheema, Mansoor Nasir and Reijgwart, Victor and Siegwart, Roland and Tombari, Federico and Cadena, Cesar},
journal={arXiv preprint arXiv:2208.08307},
year={2022}
}
```
# Video
An overview of SC-Explorer is available on [YouTube](https://youtu.be/DMXdhCqUqts):
[
](https://youtu.be/DMXdhCqUqts)
# Setup
> **ℹ️ Note**
The code is provided on an 'as-is' basis. While everything should work and is tested, photorealistic simulation and GPU inference can be cumbersome and we cannot provide support for setting this up.
## Installation
1. Install [ROS](http://wiki.ros.org/ROS/Installation) (Desktop full recommended) if not already done so.
2. Install system dependencies:
```shell script
sudo apt install python-wstool python-catkin-tools ros-$ROS_DISTRO-cmake-modules ros-$ROS_DISTRO-control-toolbox ros-$ROS_DISTRO-joy ros-$ROS_DISTRO-octomap-ros ros-$ROS_DISTRO-geographic-msgs autoconf libyaml-cpp-dev protobuf-compiler libgoogle-glog-dev liblapacke-dev libgeographic-dev
```
3. Setup catkin workspace using [catkin-tools](https://catkin-tools.readthedocs.io/en/latest/)
```shell script
mkdir -p ~/catkin_ws/src
cd ~/catkin_ws
catkin init
catkin config --extend /opt/ros/$ROS_DISTRO
catkin config --cmake-args -DCMAKE_BUILD_TYPE=Release
catkin config --merge-devel
```
5. Checkout the GitHub repository:
```shell script
cd ~/catkin_ws/src
git clone [email protected]:ethz-asl/ssc_exploration.git # Requires Git SSH
```
6. Install dependencies
```shell script
wstool init . ./ssc_exploration/.rosinstall # Requires Git SSH
wstool update
```
8. Compile
```shell script
catkin build ssc_planning
```
9. Build the scene completion network based on PAL-Net following the instructions in [`ssc_network/`](https://github.com/ethz-asl/ssc_exploration/tree/main/ssc_network).
## Simulation
The following tools were used in our experiments, other simulation or real robot setups should work, too:
* **Unreal Engine**
Setup [Unreal Engine 4.25.6](https://www.unrealengine.com/en-US/download) (UE4).
* **Airsim**
[AirSim](https://microsoft.github.io/AirSim/) is a simulation software for simulating a MAV in Unreal Engine. We use the Airsim 1.2 plugin for UE4, which is provided by unreal_airsim below.
* **unreal_airsim**
[unreal_airsim](https://github.com/ethz-asl/unreal_airsim) is a ROS interface to the simulated MAV in Unreal Engine for accessing odometry and sending trajectory commands.
* **Dataset**
The environment used in our experiments, including all plugins and ground truth can be downloaded [here](https://drive.google.com/drive/folders/1ji11IMJPlsnZQZmM4xNB3s9hnNgV0Ete?usp=sharing). Note that we can not guarantee compatibility with operating systems other than Ubuntu or different versions of AirSim/UE4.
# Experiments
## Training the Model
Instructions to train and test the SC-Network as well as pretrained weights are given in [`ssc_network/`](https://github.com/ethz-asl/ssc_exploration/tree/main/ssc_network).
## Running the Planner
1. Start Unreal Engine:
```shell script
cd # Move to the unreal install directory
./Engine/Binaries/Linux/UE4Editor your-project-file.uproject -opengl4
```
2. Start the SSC Network (Make sure the network is setup as explained in [`ssc_network/`](https://github.com/ethz-asl/ssc_exploration/tree/main/ssc_network) first):
```shell script
export SSC_DIR=/home/$USER/catkin_ws/src/ssc_exploration/ssc_network
python3 $SSC_DIR/infer_ros.py --model palnet --resume $SSC_DIR/pretrained_models/weights/PALNet.pth.tar
```
3. Launch planning pipeline with the desired planning configuration specified as argument:
```shell script
roslaunch ssc_planning run.launch planner_config_file:=sc_explorer.yaml output_directory:=path/to/output
```
Feel free to play with parameters in `sc_explorer.yaml` or use different gain evaluators by specifying them in the config. Note that all configs are composed of two files, where `baseline` specifies the shared parameters and `sc_explorer` or `exploration` provide specialized parameters for view planning.
## Evaluating an experiment
Running the pipeline as explained above will write all output files into a timestamped folder in `path/to/output`. To evaluate the recorded data and visualize the map, run:
```shell script
python3 ./ssc_mapping/src/eval/eval_plots.py [output_dir] [gt_file_path] [eval_type]
```
Where `output_dir` should point to the previously created data directory, `gt_file_path` points to the ground truth (available [here](https://drive.google.com/drive/folders/1ji11IMJPlsnZQZmM4xNB3s9hnNgV0Ete?usp=sharing) for our simulated scene), and `eval_type` is one of `tsdf` or `hierarchical` to only evaluate measured or also scene completed areas, respectively.