ICRA 2012 Paper Abstract

Close

Paper TuD08.2

Lui, Wen Lik Dennis (Monash University), Tang, Titus Jia Jie (Monash University), Drummond, Tom (University of Cambridge), Li, Wai Ho (Monash University)

Robust Egomotion Estimation Using ICP in Inverse Depth Coordinates

Scheduled for presentation during the Regular Session "RGB-D Localization and Mapping" (TuD08), Tuesday, May 15, 2012, 16:45−17:00, Meeting Room 8 (Wacipi)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on February 21, 2018

Keywords Localization, Computer Vision for Robotics and Automation, Range Sensing

Abstract

This paper presents a 6 degrees of freedom egomotion estimation method using Iterative Closest Point (ICP) for low cost and low accuracy range cameras such as the Microsoft Kinect. Instead of Euclidean coordinates, the method uses inverse depth coordinates which better conforms to the error characteristics of raw sensor data. Novel inverse depth formulations of point-to-point and point-to-plane error metrics are derived as part of our implementation. The implemented system runs in real time at an average of 28 frames per second (fps) on a standard computer. Extensive experiments were performed to evaluate different combinations of error metrics and parameters. Results show that our system is accurate and robust across a variety of motion trajectories. The point-to-plane error metric was found to be the best at coping with large inter-frame motion while remaining accurate and maintaining real time performance.

 

 

Technical Content © IEEE Robotics & Automation Society

This site is protected by copyright and trademark laws under US and International law.
All rights reserved. © 2002-2018 PaperCept, Inc.
Page generated 2018-02-21  19:17:53 PST  Terms of use