Table of Contents Author Guidelines Submit a Manuscript
Journal of Sensors
Volume 2016 (2016), Article ID 2546819, 28 pages
Research Article

Monte Carlo Registration and Its Application with Autonomous Robots

Institute of Robotics and Mechatronics, German Aerospace Center, 82234 Oberpfaffenhofen, Germany

Received 25 March 2016; Revised 28 June 2016; Accepted 10 July 2016

Academic Editor: Pablo Gil

Copyright © 2016 Christian Rink et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


This work focuses on Monte Carlo registration methods and their application with autonomous robots. A streaming and an offline variant are developed, both based on a particle filter. The streaming registration is performed in real-time during data acquisition with a laser striper allowing for on-the-fly pose estimation. Thus, the acquired data can be instantly utilized, for example, for object modeling or robot manipulation, and the laser scan can be aborted after convergence. Curvature features are calculated online and the estimated poses are optimized in the particle weighting step. For sampling the pose particles, uniform, normal, and Bingham distributions are compared. The methods are evaluated with a high-precision laser striper attached to an industrial robot and with a noisy Time-of-Flight camera attached to service robots. The shown applications range from robot assisted teleoperation, over autonomous object modeling, to mobile robot localization.