Hough transform based correction of mobile robot orientation

2005 ◽  
Author(s):  
L. Banjanovic-Mehmedovic ◽  
I. Petrovie ◽  
E. Ivanjko
Keyword(s):  
Mobile Robot ◽  
Hough Transform

Dynamic world modeling for a mobile robot among moving objects

Robotica ◽  
1996 ◽  
Vol 14 (5) ◽  
pp. 553-560
Author(s):  
Yuefeng Zhang ◽  
Robert E. Webber
Keyword(s):  
Mobile Robot ◽  
Hough Transform ◽  
Moving Objects ◽  
Grid Method ◽  
Experimental Results ◽  
Sensor Data ◽  
Probabilistic Estimation ◽  
Occupancy Grid ◽  
World Modeling ◽  
Grid Based

SUMMARYA grid-based method for detecting moving objects is presented. This method involves the extension and combination of two methods: (1) the Hough Transform and (2) the Occupancy Grid method. The Occupancy Grid method forms the basis for a probabilistic estimation of the location and velocity of objects in the scene from the sensor data. The Hough Transform enables the new method to handle non-integer velocity values. A model for simulating a sonar ring is also presented. Experimental results show that this method can handle objects moving at non-integer velocities.

Improved Hough Transform Applying in Mobile Robot Vision Navigation

2012 ◽  
Vol 232 ◽  
pp. 408-413
Author(s):  
Yin Ping Jiang ◽  
Xian Xian Zhang ◽  
Xiao Peng Fu
Keyword(s):  
Mobile Robot ◽  
Hough Transform ◽  
Robot Vision ◽  
Geometric Figure ◽  
Vision Navigation ◽  
Line Extraction ◽  
Local Threshold ◽  
Global Threshold ◽  
Image Quality Reduction ◽  
Mobile Robot Vision

This paper mainly discusses that in mobile robot vision navigation system, by using the improved Hough transform, we can improve the accuracy of line extraction and therefore avoid the image quality reduction caused by noise points. Considering the limitations of the standard Hough transform, we come up with a method with which we will accumulates the H (ρ, θ) through distributing the increment value, set a global threshold to shun the pointless measurements, eliminate the false lines by comparing θ difference between tow arbitrary lines, find the peaks by using rectangle window, and set a local threshold to eliminate false peaks. In this way, we can gain a method superior to the standard Hough transform which works better in extracting lines in application. The experiments show that this method can not only extract line features of geometric figure effectively in brief background, but also eliminate the iterative lines efficiently.

scholarly journals Mobile robot motion estimation using Hough transform

2018 ◽  
Vol 1015 ◽  
pp. 032161
Author(s):  
D N Aldoshkin ◽  
T N Yamskikh ◽  
R Yu Tsarev
Keyword(s):  
Motion Estimation ◽  
Mobile Robot ◽  
Hough Transform ◽  
Robot Motion

scholarly journals Mobile robot navigation using the range-weighted Hough transform

1995 ◽  
Vol 2 (1) ◽  
pp. 18-26 ◽  
Author(s):  
J. Forsberg ◽  
U. Larsson ◽  
A. Wernersson
Keyword(s):  
Mobile Robot ◽  
Hough Transform ◽  
Robot Navigation ◽  
Mobile Robot Navigation

scholarly journals Evaluation of Two-Dimensional Angular Orientation of a Mobile Robot by a Modified Algorithm Based on Hough Transform

2018 ◽  
Vol 18 (2) ◽  
pp. 112-122
Author(s):  
Dmitry N. Aldoshkin ◽  
Roman Y. Tsarev
Keyword(s):  
Mobile Robot ◽  
Hough Transform ◽  
Dimensional Space ◽  
Two Dimensional ◽  
Angular Orientation ◽  
Polygonal Chain ◽  
Straight Line ◽  
Robustness To Noise ◽  
Surrounding Space ◽  
Straight Lines

Abstract This paper proposes an algorithm that assesses the angular orientation of a mobile robot with respect to its referential position or a map of the surrounding space. In the framework of the suggested method, the orientation problem is converted to evaluating a dimensional rotation of the object that is abstracted as a polygon (or a closed polygonal chain). The method is based on Hough transform, which transforms the measurement space to a parametric space (in this case, a two-dimensional space [θ, r] of straight-line parameters). The Hough transform preserves the angles between the straight lines during rotation, translation, and isotropic scaling transformations. The problem of rotation assessment then becomes a one-dimensional optimization problem. The suggested algorithm inherits the Hough method’s robustness to noise.

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