The introduction of the Microsoft Kinect Sensors has stirred significant interest in the robotics community. While originally developed as a gaming interface, a high quality depth sensor and affordable price have made it a popular choice for robotic perception. Its active sensing strategy is very well suited to produce robust and high-frame rate depth maps for human pose estimation. But the shift to the robotics domain surfaced applications under a wider set of operation condition it wasn't originally designed for. We see the sensor fail completely on transparent and specular surfaces which are very common to every day household objects. As these items are of great interest in home robotics and assistive technologies, we have investigated methods to reduce and sometimes even eliminate these effects without any modification of the hardware. In particular, we complement the depth estimate within the Kinect by a cross-modal stereo path that we obtain from disparity matching between the included IR and RGB sensor of the Kinect. We investigate how the RGB channels can be combined optimally in order to mimic the image response of the IR sensor by an early fusion scheme of weighted channels as well as a late fusion scheme that computes stereo matches between the different channels independently. We show a strong improvement in the reliability of the depth estimate as well as improved performance on a object segmentation task in a table top scenario.
This issue is addressed in previous work REF by using a cross-modal stereo approach based on built-in RGB and IR sensor that complements the Kinect depth estimate.
M.: Improving the kinect by cross-modal stereo
In: British Machine Vision Conf. BMVA
Computer Science