Three-dimensional ladar imaging system using AR-4000LV laser range-finder

2011 ◽  
Author(s):  
Ali A. Al-Temeemy ◽  
J. W. Spencer
Keyword(s):  
Imaging System ◽  
Three Dimensional ◽  
Laser Range Finder ◽  
Range Finder ◽  
Laser Range

Development of a laser range finder for three-dimensional map building in rubble

2005 ◽  
Vol 19 (3) ◽  
pp. 273-294 ◽  
Author(s):  
Masamitsu Kurisu ◽  
Yasuyoshi Yokokohji ◽  
Yusuke Shiokawa ◽  
Takayuki Samejima
Keyword(s):  
Three Dimensional ◽  
Laser Range Finder ◽  
Map Building ◽  
Range Finder ◽  
Laser Range

scholarly journals PROBABILISTIC MODEL OF LASER RANGE FINDER FOR THREE DIMENSIONAL GRID CELL IN CLOSE RANGE ENVIRONMENT

2016 ◽  
Vol 17 (1) ◽  
pp. 63-82 ◽  
Author(s):  
Hafiz b Iman ◽  
NAHRUL KHAIR ALANG MD RASHID
Keyword(s):  
Probabilistic Model ◽  
Incident Angle ◽  
Three Dimensional ◽  
Grid Cell ◽  
Normal Density ◽  
Laser Range Finder ◽  
Map Building ◽  
Range Finder ◽  
Laser Range ◽  
Close Range

The probabilistic model of a laser scanner presents an important aspect for simultaneous localization and map-building (SLAM). However, the characteristic of the beam of the laser range finder under extreme incident angles approaching 900 has not been thoroughly investigated. This research paper reports the characteristic of the density of the range value coming from a laser range finder under close range circumstances where the laser is imposed with a high incident angle. The laser was placed in a controlled environment consisting of walls at a close range and 1000 iteration of scans was collected. The assumption of normal density of the metrical data collapses when the beam traverses across sharp edges in this environment. The data collected also shows multimodal density at instances where the range has discontinuity. The standard deviation of the laser range finder is reported to average at 10.54 mm, with 0.96 of accuracy. This significance suggests that under extreme incident angles, a laser range finder reading behaves differently compared to normal distribution. The use of this information is crucial for SLAM activity in enclosed environments such as inside piping grid or other cluttered environments.KEYWORDS:   Hokuyo UTM-30LX; kernel density estimation; probabilistic model  

scholarly journals Calibration of Short Range 2D Laser Range Finder for 3D SLAM Usage

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Petr Olivka ◽  
Michal Krumnikl ◽  
Pavel Moravec ◽  
David Seidl
Keyword(s):  
Short Range ◽  
Three Dimensional ◽  
Ground Truth ◽  
Calibration Method ◽  
Calibration Procedure ◽  
High Angular Resolution ◽  
Laser Range Finder ◽  
Range Finder ◽  
Laser Range ◽  
Practical Applications

The laser range finder is one of the most essential sensors in the field of robotics. The laser range finder provides an accurate range measurement with high angular resolution. However, the short range scanners require an additional calibration to achieve the abovementioned accuracy. The calibration procedure described in this work provides an estimation of the internal parameters of the laser range finder without requiring any special three-dimensional targets. This work presents the use of a short range URG-04LX scanner for mapping purposes and describes its calibration. The precision of the calibration was checked in an environment with known ground truth values and the results were statistically evaluated. The benefits of the calibration are also demonstrated in the practical applications involving the segmentation of the environment. The proposed calibration method is complex and detects all major manufacturing inaccuracies. The procedure is suitable for easy integration into the current manufacturing process.

Using Laser Range Finder Obstacle Avoidance and Using TLD Positioning System of Mobile Robot

2015 ◽  
Vol 764-765 ◽  
pp. 730-734
Author(s):  
Jr Hung Guo ◽  
Kuo Lan Su
Keyword(s):  
Obstacle Avoidance ◽  
Imaging System ◽  
Recognition System ◽  
Laser Range Finder ◽  
Range Finder ◽  
Detection Range ◽  
Laser Range ◽  
Sensing System ◽  
Reflection Data ◽  
Reflectance Data

Robot self-localization and obstacle avoidance has been one of the important topics in robotics. The sensing system which is more mature and using a laser range finder (LRF). But the biggest drawback is the LRF detection range is a plane, And for some high reflectivity of the object, will produce incorrect reflection data. So when the obstacle is not the detection range, or due to high reflectance data will generate an error and the positioning of the robot obstacle avoidance function error. This paper is the use of TLD (Tracking-Learning-Detection) image recognition system, to assist LRF do positioning and obstacle avoidance. And this imaging system can also be used while the robot with object tracking functions

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