Abstract
Projectors have become a widespread tool to share information in Human-Robot Interaction with large groups of people in a comfortable way. Finding a suitable vertical surface becomes a problem when the projector changes positions when a mobile robot is looking for suitable surfaces to project. Two problems must be addressed to achieve a correct undistorted image: (i) finding the biggest suitable surface free from obstacles and (ii) adapting the output image to correct the distortion due to the angle between the robot and a nonorthogonal surface. We propose a RANSAC-based method that detects a vertical plane inside a point cloud. Then, inside this plane, we apply a rectangle-fitting algorithm over the region in which the projector can work. Finally, the algorithm checks the surface looking for imperfections and occlusions and transforms the original image using a homography matrix to display it over the area detected. The proposed solution can detect projection areas in real-time using a single Kinect camera, which makes it suitable for applications where a robot interacts with other people in unknown environments. Our Projection Surfaces Detector and the Image Correction module allow a mobile robot to find the right surface and display images without deformation, improving its ability to interact with people.
Highlights
An interesting possibility of enhancing Human-Robot Interaction (HRI) by offering information to the user is to embark a video projector in a mobile robot
Because of the occlusion generated by the closet and the error introduced by the camera, there is going to be times when the biggest surface detected with Random Sample Consensus (RANSAC) will be the wall and other times when it would be the closet
We have presented a system that allows a robot equipped with a projector and a Kinect to find projection surfaces, adapting the output image to correct the distortion due to the angle between the robot and a nonorthogonal surface
Summary
An interesting possibility of enhancing Human-Robot Interaction (HRI) by offering information to the user is to embark a video projector in a mobile robot. If we want to deploy this technology in a mobile robot, two issues arise derived from projecting on unknown surfaces: (i) the main challenge is the need to place the robot in front of a planar surface big enough to fit the projected content This is an important issue, especially in real environments where those surfaces might not be always suitable for projecting; and (ii) another significant problem is that following the usual way of projecting, the robot needs to be placed exactly on an axis perpendicular to the selected surface in order to avoid the Keystone effect [1]: the deformation of the image is caused by trying to project it onto a surface at an angle
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