Outdoor Mapping Using Mobile Robots and Laser Range Finders

2009 ◽  
Author(s):  
Alberto Yukinobu Hata ◽  
Denis Fernando Wolf
Keyword(s):  
Mobile Robots ◽  
Laser Range ◽  
Laser Range Finders

scholarly journals Simultaneous Tracking of Multiple Targets Using SIR/MCMC Particle Filters by Distributed Cameras and Laser Range Finders

2010 ◽  
Vol 28 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Ryo Kurazume ◽  
Hiroyuki Yamada ◽  
Koji Sokabe ◽  
Kouji Murakami ◽  
Yumi Iwashita ◽  
...  
Keyword(s):  
Particle Filters ◽  
Multiple Targets ◽  
Laser Range ◽  
Laser Range Finders ◽  
Distributed Cameras

Motion planning for mobile robots using a laser range finder

2009 ◽  
Vol 14 (2) ◽  
pp. 257-261 ◽  
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

MULTIPLE-TARGET CLASSIFICATION AND TRACKING FOR MOBILE ROBOTS USING A 2D LASER RANGE SCANNER

2012 ◽  
Vol 09 (04) ◽  
pp. 1250025 ◽  
Author(s):  
POLYCHRONIS KONDAXAKIS ◽  
HARIS BALTZAKIS
Keyword(s):  
Mobile Robots ◽  
Real Time ◽  
Moving Objects ◽  
Data Association ◽  
Human Robot Interaction ◽  
Range Data ◽  
Multiple Model ◽  
Target Classification ◽  
Laser Range ◽  
Fuzzy Art

In human–robot interaction developments, detection, tracking and identification of moving objects (DATMO) constitute an important problem. More specifically, in mobile robots this problem becomes harder and more computationally expensive as the environments become dynamic and more densely populated. The problem can be divided into a number of sub-problems, which include the compensation of the robot's motion, measurement clustering, feature extraction, data association, targets' trajectory estimation and finally, target classification. Here, a mobile robot uses 2D laser range data to identify and track moving targets. A Joint Probabilistic Data Association with Interacting Multiple Model (JPDA-IMM) tracking algorithm associates the available laser data to track and provide an estimated state vector of targets' position and velocity. Potential moving objects are initially learned in a supervised manner and later on are autonomously classified in real-time using a trained Fuzzy ART neural network classifier. The recognized targets are fed back to the tracker to further improve the track initiation process. The resulting technique introduces a computationally efficient approach to already existing target-tracking and identification research, which is especially suited for real time application scenarios.

Sign in / Sign up

Export Citation Format

Share Document