  We design a multiscopic vision system that utilizes a low-cost monocular RGB
camera to acquire accurate depth estimation for robotic applications. Unlike
multi-view stereo with images captured at unconstrained camera poses, the
proposed system actively controls a robot arm with a mounted camera to capture
a sequence of images in horizontally or vertically aligned positions with the
same parallax. In this system, we combine the cost volumes for stereo matching
between the reference image and the surrounding images to form a fused cost
volume that is robust to outliers. Experiments on the Middlebury dataset and
real robot experiments show that our obtained disparity maps are more accurate
than two-frame stereo matching: the average absolute error is reduced by 50.2%
in our experiments.
