2P1-C30 Localization and Mapping for Fast Mobile Robots with 2D Laser Range Finder

SUMMARYThis paper describes a landmark detection and localization using an integrated laser-camera sensor. Laser range finder can be used to detect landmarks that are direction invariant in the laser data such as protruding edges in walls, edges of tables, and chairs. When such features are unavailable, the dependant processes will fail to function. However, in many instances, larger number of landmarks can be detected using computer vision. In the proposed method, camera is used to detect landmarks while the location of the landmark is measured by the laser range finder using laser-camera calibration information. Thus, the proposed method exploits the beneficial aspects of each sensor to overcome the disadvantages of the other sensor. While highlighting the drawbacks and limitations of single sensor based methods, an experimental results and important statistics are provided for the verification of the affectiveness sensor fusion method using Extended Kalman Filter (EKF) based simultaneous localization and mapping (SLAM) as an example application.

Download Full-text

Motion planning for mobile robots using a laser range finder

Artificial Life and Robotics ◽

10.1007/s10015-009-0666-1 ◽

2009 ◽

Vol 14 (2) ◽

pp. 257-261 ◽

Cited By ~ 4

Author(s):

Jr-Hung Guo ◽

Kuo-Lan Su ◽

Chia-Ju Wu ◽

Sheng-Ven Shiau

Keyword(s):

Motion Planning ◽

Mobile Robots ◽

Laser Range Finder ◽

Range Finder ◽

Laser Range

Download Full-text

Furniture Model Creation Through Direct Teaching to a Mobile Robot

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2008.p0213 ◽

2008 ◽

Vol 20 (2) ◽

pp. 213-220 ◽

Cited By ~ 6

Author(s):

Kimitoshi Yamazaki ◽

◽

Takashi Tsubouchi ◽

Masahiro Tomono ◽

◽

...

Keyword(s):

Mobile Robot ◽

Mobile Robots ◽

Real Life ◽

Experimental Results ◽

Sensor Data ◽

Small Object ◽

Laser Range Finder ◽

Range Finder ◽

Laser Range ◽

Direct Teaching

In this paper, a modeling method to handle furniture is proposed. Real-life environments are crowded with objects such as drawers and cabinets that, while easily dealt with by people, present mobile robots with problems. While it is to be hoped that robots will assist in multiple daily tasks such as putting objects in into drawers, the major problems lies in providing robots with knowledge about the environment efficiently and, if possible, autonomously.If mobile robots can handle these furniture autonomously, it is expected that multiple daily jobs, for example, storing a small object in a drawer, can be performed by the robots. However, it is a perplexing process to give several pieces of knowledge about the furniture to the robots manually. In our approach, by utilizing sensor data from a camera and a laser range finder which are combined with direct teaching, a handling model can be created not only how to handle the furniture but also an appearance and 3D shape. Experimental results show the effectiveness of our methods.

Download Full-text