We are pleased to announce our Deep Neural Net object detection node dnn_detect.
For robots to be useful servants, they need to be able to recognize objects so that actions on and with those objects can be programmed. For example, in our robotic cocktail waiter application, the robot has to be able to find people in the room to serve.
Because this is important functionality, we have developed a generalized deep neural net node that can recognize twenty common household objects using monocular camera data. The results look like this:
Our node uses the [Deep Neural Network module in OpenCV] (https://docs.opencv.org/3.3.0/d2/d58/tutorial_table_of_content_dnn.html) to find a variety of objects using a pretrained model. The class of the objects detected, bounding boxes, and the confidence of the classifications are published as a ROS topic. This allows the robot to interact with its environment in meaningful ways.
Have an issue, or an idea for improvement? Open an issue or PR on the GitHub repo.
This package will be part of the robots that we recently released via crowdfunding on Indiegogo.
The Ubiquity Robotics Team