Predicting object occupancy on the floor from RGBD images of indoor scenes

This thesis presents an approach to predict the occupied area on the floor in an image of an indoor scene. The goal is to be able to obtain navigable areas even in cluttered indoor environments. This algorithm could be used in the field of robotics where robots need to navigate through a room while being mindful of the surrounding objects. The results are quite close to the ground truth, as exemplified by the false positive, false negative, precision and recall rates. Using this algorithm improves the label predictions.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2013

Author dc:creator

• Bhobe, Sujay

Contributors dc:contributor

• Hoiem, Derek W.

• object occupancy
• graph cuts
• alpha expansion
• label prediction
• overhead view
• scene understanding
• computer vision

Statement dc:rights

• Copyright 2013 Sujay Uday Bhobe

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/44195

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/44195