Position estimation based on the target shape information using laser range finders for Intelligent Space

2010 ◽  
Author(s):  
Takeshi Sasaki ◽  
Hajime Tamura ◽  
Hideki Hashimoto ◽  
Fumihiro Inoue
Keyword(s):  
Position Estimation ◽  
Shape Information ◽  
Laser Range ◽  
Target Shape ◽  
Intelligent Space ◽  
Laser Range Finders

scholarly journals Position Estimation using Environment-installed Laser Range Finders and Traveling Control for Autonomous Surface Compression Work of Vibration Roller

2020 ◽  
Vol 38 (9) ◽  
pp. 872-881
Author(s):  
Kazunori Kikuchi ◽  
Keiji Nagatani ◽  
Tomohiro Komatsu ◽  
Seiga Kiribayashi ◽  
Kimitaka Asano ◽  
...  
Keyword(s):  
Position Estimation ◽  
Laser Range ◽  
Laser Range Finders ◽  
Surface Compression

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

Development of Autonomous Robot with Simple Navigation System for Tsukuba Challenge 2015

2016 ◽  
Vol 28 (4) ◽  
pp. 432-440 ◽  
Author(s):  
Yuta Kanuki ◽  
◽  
Naoya Ohta
Keyword(s):  
Computational Cost ◽  
Map Matching ◽  
Shape Information ◽  
Environment Map ◽  
Rainy Weather ◽  
Laser Range Finders ◽  
Fixed Type ◽  
Sensor Configuration ◽  
Binary Image Processing ◽  
And Control

[abstFig src='/00280004/01.jpg' width='300' text='MercuryMega (SICKLaser-Model)' ] This paper introduces the robot that was developed for Tsukuba Challenge 2015. One of the team’s design goals was to implement a simple sensor configuration and control algorithm. For example, the robot’s laser range finders (LRF) were of fixed type, and localization was accomplished using only a single LRF and no other sensor. Environment map matching was achieved using an algorithm that processed pyramid images using binary image processing to reduce computational cost. Shape information from the LRF and color information from a camera were combined to detect a signboard placed near a person. During an actual test in rainy weather, the robot ran the entire course and, detected two, out of a possible four, number of target persons.

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