BraccioVisualAttention

This project uses a ZED stereo camera mounted on a Braccio robotic arm to build accurate 3D models of environments autonomously using visual attention mechanisms. This setup can detect interesting points and objects within an unknown environment, and directs the camera's gaze to focus on these points.

Installation

Requirements

• Ubuntu 16.04 LTS
• ROS Kinetic
• ZED SDK

Steps

Create a catkin workspace:

mkdir -p ~/catkin_ws/src

Navigate to the src directory in your catkin workspace and clone this repository:

cd ~/catkin_ws/src
git clone https://github.com/KB9/BraccioVisualAttention.git

Build the project using catkin:

cd ~/catkin_ws
catkin_make install

Usage

There are 4 parts to this project:

• braccio_gaze_control: This is responsible for the communication between your computer and the Braccio's Arduino.
• environment_analysis: Performs analysis on the data from the camera feed, and selects the most important features to look at.
• tf_object_detection: A ROS wrapper for the TensorFlow Object Detection API, allowing objects to be detected in the camera feed.
• zed-ros-wrapper: A modified version ROS wrapper for the ZED stereo camera, whose modification allows it to generate and save an environment mesh.

Communication with the Braccio

To send movement commands to the Braccio, the Braccio's Arduino Yun must be first connected to the same Wi-Fi hotspot as your computer:

• Connect to the Wi-Fi hotspot created by the Yun.
• Visit http://arduino3.local
• Follow the instructions to connect the Yun to the Wi-Fi hotspot your computer is connected to.

Once the Yun is connected to the same Wi-Fi hotspot, the JSON RPC server must be set up on the Braccio's Arduino Yun. The server code can be sent to the Arduino as follows:

roscd braccio_gaze_control/
scp braccio_gaze_server.py [email protected]:~

This will copy the server code into root's home directory on the Arduino. To launch the RPC server, SSH into the Arduino, using arduino as the password:

ssh [email protected]

Then launch the server:

python braccio_gaze_server.py

If this was successful, you should see a confirmation message stating that the server is running at a specified IP address and port.

Now you must connect a client to the server, in order to send commands from your computer to the running server:

cd ~/catkin_ws/
source devel/setup.bash
rosrun braccio_gaze_control braccio_gaze_controller.py

Starting the ZED Camera

The ZED stereo camera must be started before any environment analysis can be performed:

cd ~/catkin_ws
roslaunch zed_wrapper zed.launch

If successful, this should report that data is being published on several ROS topics.

Starting the TensorFlow Object Detection Service

This is optional, as the environment analysis can be performed without object detection.

To start the object detection service:

cd ~/catkin_ws
rosrun tf_object_detection object_detection_demo.py

If successful, a message stating that the service is ready for object detection will be displayed.

Starting the Environment Analyser

To start the environment analyser:

cd ~/catkin_ws
rosrun environment_analysis mapper

A window displaying the camera feed and various debug-related data should be displayed, indicating that the environment analysis is currently active.