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Sigma-FP: Robot Mapping of 3D Floor Plans with an RGB-D Camera under Uncertainty

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# Sigma-FP: Robot Mapping of 3D Floor Plans with an RGB-D Camera under Uncertainty

<a href="https://mapir.isa.uma.es/mapirwebsite/?p=1792">Jose-Luis Matez-Bandera</a>1, <a href="https://mapir.isa.uma.es/mapirwebsite/?p=1438">Javier Monroy</a>1 and <a href="http://mapir.isa.uma.es/jgonzalez">Javier Gonzalez-Jimenez</a>1

1 Machine Perception and Intelligent Robotics (MAPIR) Group, Malaga Institute for Mechatronics Engineering and Cyber-Physical Systems (IMECH.UMA). University of Malaga. Spain.

### Content
 <a href="#citation">Citation</a>&nbsp;&nbsp;&nbsp;<a href="#installationandrequirements">Installation&nbsp;and&nbsp;Requirements</a>&nbsp;&nbsp;&nbsp;<a href="#configuration">Configuration</a>&nbsp;&nbsp;&nbsp;<a href="#howtorun">How&nbsp;to&nbsp;Run</a>&nbsp;&nbsp;&nbsp;<a href="#datasets">Datasets</a>

### Citation
<pre><code>@ARTICLE{matez_sigmafp,
 author={Matez-Bandera, Jose-Luis and Monroy, Javier and Gonzalez-Jimenez, Javier},
 journal={IEEE Robotics and Automation Letters}, 
 title={Sigma-FP: Robot Mapping of 3D Floor Plans With an RGB-D Camera Under Uncertainty}, 
 year={2022},
 volume={7},
 number={4},
 pages={12539-12546},
 doi={10.1109/LRA.2022.3220156}}
</code></pre>

### Installation&nbsp;and&nbsp;Requirements

Clone the repository in the /src directory of your ROS workspace:

<code>git clone https://github.com/MAPIRlab/Sigma-FP.git</code>

Sigma-FP has been released as a ROS package and works with Python 2.7. To install requirements, execute:

<code>cd Sigma-SP</code> 
<code>pip install -r requirements.txt</code>

Additionally, it is required to install the following ROS packages:

- [cv_bridge](http://wiki.ros.org/cv_bridge)
- [tf2_ros](http://wiki.ros.org/tf2_ros)
- [tf](http://wiki.ros.org/tf)
- [message_filters](http://wiki.ros.org/message_filters)

Build ROS workspace:

<code>cd ~/your_ros_workspace</code> 
<code>cd catkin_make</code>

Sigma-FP requires a per-pixel semantic segmentation network to run. We have employed Detectron2, but any other per-pixel semantic segmentation network can be used, although Sigma-FP code will need to be slightly adapted. In case you wish to use Detectron2, we have released our adaptation in the following repository: [detectron2_ros_probs](https://github.com/josematez/detectron2_ros_probs). The installation instructions are available in the repository.

### Configuration

Sigma-FP parameters are configured using launch parameters. The configurable parameters are:

## Parameters
```bash
 # Name of the dataset to use (options: "RobotAtVirtualHome", "OpenLORIS", "Giraff" (this is for MAPIRlab) - leave empty for custom dataset)
 <param name="dataset" value="Giraff"/>
 # Topic where the RGB image is published
 <param name="topic_cameraRGB" value="camera_down/rgb/image_raw/compressed"/>
 # Topic where the Depth image is published
 <param name="topic_cameraDepth" value="/camera_down/depth/image"/>
 # Topic where the CNN results are published
 <param name="topic_result" value="ViMantic/Detections"/>
 # Topic where the CNN expects to receive the input image
 <param name="topic_republic" value="ViMantic/ToCNN"/>
 # Topic where the CNN publish the image including detections
 <param name="topic_cnn" value="detectron2_ros/result"/>
 # Debug option
 <param name="debug" value="false"/>
 # Image width
 <param name="image_width" value="640"/>
 # Image height
 <param name="image_height" value="480"/>
 # Intrinsic parameters of the camera
 <param name="camera_cx" value="318.2640075683594"/>
 <param name="camera_cy" value="237.88600158691406"/>
 <param name="camera_fx" value="510.3919982910156"/>
 <param name="camera_fy" value="510.3919982910156"/>
 # Max range of the depth camera
 <param name="camera_depth_max_range" value="10.0"/>
 # Number of desired point to downsample each input point cloud
 <param name="points_in_pcd" value="4000"/>
 # Minimum number of points to accept a planar patch as a candidate
 <param name="min_points_plane" value="100"/>
 # Minimum width (in meters) of a planar patch to accept it as a candidate
 <param name="min_plane_width" value="0.6"/>
 # Minimum number of pixels to consider a region as a opening in the image plane
 <param name="min_px_opening" value="8000"/>
 # Threshold for the statistical distance of Bhattacharyya
 <param name="bhattacharyya_threshold" value="7"/>
 # Threshold for the minimum euclidean distance between walls (in meters)
 <param name="euclidean_threshold" value="0.3"/>
 # Epsilon for DBSCAN of the azimuth angle of the plane (in radians)
 <param name="eps_alpha" value="1.0"/>
 # Epsilon for DBSCAN of the elevation angle of the plane (in radians)
 <param name="eps_beta" value="10.0"/>
 # Epsilon for DBSCAN of the plane-to-origin distance (in meters)
 <param name="eps_dist" value="0.02"/>
 
```

### How&nbsp;to&nbsp;Run

First, run the semantic segmentation neural network. For example, if you are using our recommended neural network ([Detectron2](https://github.com/josematez/detectron2_ros_probs)), first you need to activate the virtual environment:

<code>workon detectron2_ros</code>

Then, execute the launch file:

<code>roslaunch detectron2_ros panoptic_detectron2_ros.launch</code>

Once the semantic segmentation network is ready, you can run Sigma-FP as follows*:

<code>roslaunch sigmafp MAPIRlab.launch</code>

*Note that it is an example with the MAPIRlab dataset. For custom data, please create a launch file following the examples given in the launch directory.

### Datasets

If you are interested in reproducing results, please contact us at <a>josematez@uma.es</a> to provide the employed datasets.

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