Content-Type: text/html R-LAIR: Riverside Lab for Artificial Intelligence Research

UCR

Computer Science and Engineering



R-LAIR: Riverside Lab for Artificial Intelligence Research

SLAM in Large Indoor Environments with Low-Cost, Noisy, and Sparse Sonars (2009)

by Teddy N. Yap, Jr. and Christian R. Shelton

Abstract: Motion and sensor models are crucial components in current algorithms for mobile robot localization and mapping. These models are typically provided and hand-tuned by a human operator and are often derived from intensive and careful calibration experiments and the operator's knowledge and experience with the robot and its operating environment. In this paper, we demonstrate how the parameters of both the motion and sensor models can be automatically estimated during normal robot operations via machine learning methods thereby eliminating the necessity of manually tuning these models through a laborious calibration process. Results from real-world robotic experiments are presented that show the effectiveness of the estimation approach.

Download Information

Teddy N. Yap, Jr. and Christian R. Shelton (2009). "SLAM in Large Indoor Environments with Low-Cost, Noisy, and Sparse Sonars." Proceedings of the IEEE International Conference on Robotics and Automation (pp. 1395-1401). pdf ps ps.gz  

Bibtex citation

@inproceedings{YapShe09,
   author = "Yap, Jr., Teddy N. and Christian R. Shelton",
   title = "{SLAM} in Large Indoor Environments with Low-Cost, Noisy, and Sparse Sonars",
   booktitle = "Proceedings of the {IEEE} International Conference on Robotics and Automation",
   booktitleabbr = "{ICRA}-2009",
   year = 2009,
   pages = "1395--1401"
}

full list

More Information

Address

University of California, Riverside
Chung Hall, room 368
Riverside, CA 92521

 


External Links