Visual Place Categorization: Problem, Dataset, and Algorithm
Author(s)
Advisor(s)
Editor(s)
Collections
Supplementary to:
Permanent Link
Abstract
In this paper we describe the problem of Visual
Place Categorization (VPC) for mobile robotics, which
involves predicting the semantic category of a place from image
measurements acquired from an autonomous platform. For
example, a robot in an unfamiliar home environment should be
able to recognize the functionality of the rooms it visits, such
as kitchen, living room, etc. We describe an approach to VPC
based on sequential processing of images acquired with a conventional
video camera.We identify two key challenges: Dealing
with non-characteristic views and integrating restricted-FOV
imagery into a holistic prediction. We present a solution to
VPC based upon a recently-developed visual feature known as
CENTRIST (CENsus TRansform hISTogram). We describe a
new dataset for VPC which we have recently collected and are
making publicly available. We believe this is the first significant,
realistic dataset for the VPC problem. It contains the interiors
of six different homes with ground truth labels. We use this
dataset to validate our solution approach, achieving promising
results.
Sponsor
Date
2009-10
Extent
Resource Type
Text
Resource Subtype
Article