Turtlebot Followbot, EECE 5698 Final Project, Spring 2018

This is the final project for EECE 5698, Robotics Sensing and Navigation with Prof. Singh. The goal of this project was to develop a robot capable of following a user using various methods of person detection on the Turtlebot3 Platform.

Google Drive Presentation

Person Detection

We evaluated methods of person detection using both LIDAR and camera sensors.

Camera

We implemented two forms of person detection using camera. Each method pulled ROS Image messages from the /raspicam2/image_raw topic and published the ROI to follow to the /roi topic. If more than one ROI was found, only the ROI with the largest area was published.

A diagram of the node/topic map is shown below.

HOG Detector: The first method we used was OpenCVs built in pedestrian HoG Detector. After detection, we perform a non-maxima supression to prevent the same object from being detected more than once. While this method worked, it proved fairly inconsistent, and required the pedestrian be in ideal lighting and focus conditions. Any occlusion or change in position severely impacted the performance of the detector. Furthermore, the HoG Detector also had a fair number of false positives.

MobilenetSSD: The second detector we used was the MobileNetSSD network using OpenCV 3.3s built in DNN support. This network proved to be much more accurate and was capable of detecting people in a number of different positions, including when parts of the body were occluded. Furthermore, MobileNet was able to classify a number of other object, in addition to people. While we did not use these other labels, this feature could be an interesting topic to explore in future projects.

Lidar

We used a pre-trained random forest classifier to detect a person using the sensor data from the Turtlebot's LIDAR. While this classifier was originally trained to detect a person at 50cm, we were able to modify some of the filter parameters to detect a person up to 1m. Unfortunately, the LIDAR on the turtlebot is an inexpensive, low resolution 2D LIDAR that has a range of 3.5m and only takes one point/degree. With such a low resolution detecting a person beyond 1m becomes exceptionally difficult.

Setup Dependencies

• [turtlebot3][https://github.com/ROBOTIS-GIT/turtlebot3]
• [turtlebot3_msgs][https://github.com/ROBOTIS-GIT/turtlebot3_msgs]
• [raspicam_node][https://github.com/UbiquityRobotics/raspicam_node]
• [gscam (for use with GStreamer)][https://github.com/ros-drivers/gscam]
• OpenCV 3.3

Remote PC Side

• Need to be running the NTP server to allow the TurtleBot to synchronize its timing when offline
• Ensure $ROS_MASTER_URI is set to the ip address of your machine
• Run roscore on the remote pc side. The remote machine is the master node.
• Launch turtlebot3_remote node
• Launch gscam node
• Run whichever detector node you want (dnn or hog)
• Run follow_bot node (reads /roi, publishes to /cmd_vel)

TurtleBot Side

• Ensure $ROS_MASTER_URI is set to the ip address of your remote machine
• Ensure the date and time are correctly set (i.e. synchronized) with the master node
• Run turtlebot3_bringup node
• Stream the video from the Raspberry picam to the GStreamer Node