Category: CMUTR
Institution: Department of Computer Science, Carnegie
        Mellon University
Abstract: As mobile robots attempt more difficult tasks in more complex
        environments, they are faced with combinatorially harder 
        perceptual problems.
        In fact, computation costs for perception can easily dominate
        the costs for planning in a mobile robot.
        Existing perception systems on mobile robots are potentially 
        many orders of magnitude too slow for real-world domains.
        In this paper we show active vision at the system level can make
        perception more tractable.
        We describe how our planning system for a complex domain, 
        tactical driving, makes specific perceptual requests to find 
        objects of interest.
        The perception system then scans the scene using routines to 
        search for these objects in limited areas.
        The selective vision is based on an understanding and analysis 
        of the driving task.
        We illustrate the effectiveness of request-driven routines by 
        comparing the computational cost of general scene analysis with
        that of selective vision in simulated driving situations.
Number: CMU-CS-91-199
Bibtype: TechReport
Month: nov
Author: Douglas A. Reece
        Steven Shafer
Title: Using Active Vision to Simplify Perception
Year: 1991
Address: Pittsburgh, PA
Super: @CMUTR