Dimensionality Reduction Using Automatic Supervision for Vision-Based Terrain Learning

Abstract— This paper considers the problem of learning to recognize different terrains from color imagery in a fully automatic fashion, using the robot’s mechanical sensors as supervision. We present a probabilistic framework in which the visual information and the mechanical supervision interact to learn the available terrain types. Within this framework, a novel supervised dimensionality reduction method is proposed, in which the automatic supervision provided by the robot helps select better lower dimensional representations, more suitable for the discrimination task at hand. Incorporating supervision into the dimensionality reduction process is important, as some terrains might be visually similar but induce very different robot mobility. Therefore, choosing a lower dimensional visual representation adequately is expected to improve the vision-based terrain learning and the final classification performance. This is the first work that proposes automatically supervised dimens...

• Type: Conference
• Year: 2007
• Where: RSS
• Authors: Anelia Angelova, Larry Matthies, Daniel M. Helmick, Pietro Perona
• Keywords: Dimensionality Reduction, Lower Dimensional Visual, Robotics, RSS 2007, Terrain