Updated README (#11)

README.md

@@ -1,23 +1,65 @@
+
 # articulated_objects_scene_builder
 
-## About
-ROS node that builds point cloud-based model of the scene enhanced with information about articulated objects. It subscribes to following topics:
-- topic with point cloud to process that can be set using `rosparam set rosparam set input_point_cloud_topic "input_point_cloud_topic"`
-- `front_prediction` which contains information about detected fronts of articulated objects. [See node](https://github.com/arekmula/ros_front_detection_segmentation)
-- `handler_prediction_topic` which contains information about detected handlers of articulated objects. [See node](https://github.com/arekmula/ros_handler_detector)
-- `joint_prediction_topic` which contains information about detected joints of articulated objects that are rotational. [See node](https://github.com/arekmula/ros_joint_segmentation)
+<p align="center">
+  <img alt="1" src="imgs/object1.gif" width="30%">
+&nbsp; &nbsp; &nbsp; &nbsp;
+  <img alt="2" src="imgs/object2.gif" width="30%">
+&nbsp; &nbsp; &nbsp; &nbsp;
+  <img alt="2" src="imgs/object3.gif" width="30%">
+</p>
+
+The goal of the project is to build a ROS system that would be able to build a point-cloud-based model of the scene enhanced with information about articulated objects based on a **single** RGB-D image. The articulated objects defined by this work are drawers and cabinets.
+
+This module is part of my master thesis "Point cloud-based model of the scene enhanced with information about articulated objects"
+
+This node concatenates results and runs on top of the previous three nodes that are available here:
+
+- [Handler detector](https://github.com/arekmula/ros_handler_detector)
+- [Front detector](https://github.com/arekmula/ros_front_detection_segmentation)
+- [Rotational joint detector](https://github.com/arekmula/ros_joint_detection)
+
+to build a final model of the scene in a 3D environment.
+
+The node subsribes to the following topics:
+- topic with Microsoft Kinect point cloud, that can be set using `rosparam set input_point_cloud_topic "input_point_cloud_topic"`
+- `front_prediction` which contains information about detected fronts of articulated objects. 
+- `handler_prediction_topic` which contains information about detected handlers of articulated objects. 
+-  `joint_prediction_topic` which contains information about detected joints of articulated objects that are rotational. 
 
 The node publish following topics:
-- `image_to_process` - RGB image obtained from input point cloud, which can be processed by external nodes
-- `currently_processed_point_cloud` - currently processed point cloud
-- `processed_point_cloud` - processed point cloud with marked data
+- `image_to_process` - RGB image obtained from input point cloud, which has to be processed by the rest of the nodes
+- `cloud_to_process` - currently processed point cloud
+- `processed_fronts_point_cloud` - Processed point cloud with fronts data
+- `processed_handlers_point_cloud` - Processed point cloud with handlers data
+- `trans_fronts_normals` - Normals of transitional objects
+- `rot_front_joints` - Rotational joints
+- `last_processed_point_cloud` - Point cloud that was processed a moment ago
+
+## Dataset
+The dataset that was used using developing the project is available here:
+- [part1](https://drive.google.com/file/d/1fhE5tN_5AM1CKty76QT63WNsISecXsl7/view?usp=sharing)
+- [part2](https://drive.google.com/file/d/1k008_vaWegVhvY-ULVqCNqPQ12pFojvO/view?usp=sharing)
 
+The dataset consists of 38 rosbag sequences, which contain 32 unique transitional objects and 53 unique rotational objects.
+## Results
+
+The results gathered on the dataset above looks as follow:
+-   Mean time of scene building: 1.9393s
+```markdown
+|                                         | Transitional objects | Rotational Objects |
+|:---------------------------------------:|:--------------------:|--------------------|
+|   Found fronts/Total number of fronts   |         31/32        |        50/53       |
+|   Found joints/Total number of joints   |         31/32        |        46/53       |
+| Found handlers/Total number of handlers |         39/40        |        50/53       |
+```
 ## Dependencies
 - ROS Noetic
 - PCL library `sudo apt install libpcl-dev`
 
 ## Run with
 ```
-rosparam set rosparam set input_point_cloud_topic "input_point_cloud_topic"
+rosparam set input_point_cloud_topic "input_point_cloud_topic"
 roslaunch model_builder model_builder.launch 
 ```
+Run nodes responsible for handler, front and rotational joint detection.