Optical Flow Templates for Superpixel Labeling in Autonomous Robot Navigation
Abstract
Instantaneous image motion in a camera on-board
a mobile robot contains rich information about the structure
of the environment. We present a new framework, optical flow
templates, for capturing this information and an experimental
proof-of-concept that labels superpixels using them. Optical
flow templates encode the possible optical flow fields due to
egomotion for a specific environment shape and robot attitude.
We label optical flow in superpixels with the environment shape
they image according to how consistent they are with each template. Specifically, in this paper we employ templates highly relevant to mobile robot navigation. Image regions consistent
with ground plane and
distant structure
templates likely indicate
free and traversable space, while image regions consistent with
neither of these are likely to be nearby objects that are obstacles. We evaluate our method qualitatively and quantitatively in an
urban driving scenario, labeling the ground plane, and obstacles
such as passing cars, lamp posts, and parked cars. One key
advantage of this framework is low computational complexity, and we demonstrate per-frame computation times of 20ms, excluding optical flow and superpixel calculation.