Clustering and line detection in laser range measurements

Fernández, Carlos; Moreno, Vidal; Curto, Belén; Vicente, J. Andrés

doi:10.1016/j.robot.2009.10.008

Cited by 26 publications

(13 citation statements)

References 13 publications

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“…Obtain straight-line maps by applying SVD or Hough extraction of the corners, other techniques like the split and merge [8,9,45] and IEPF [33] algorithms discussed earlier can be used.…”

Section: A Hough Transform Is a Technique For Line Extraction From Bimentioning

confidence: 99%

“…The Hough transform generates a straight line by looking into each LRF data point and, if there is enough evidence of an edge on that point, the Hough transform calculates the parameters for that line and increments the corresponding Researchers have proposed many solutions [9,44,14,23] for obtaining the most optimum parameter value by which a single straight line can be produced. Most of them are proposed for finding straight lines in real images for boundary or feature extraction.…”

Section: Clustering In the Hough Domain (Accumulator Space)mentioning

confidence: 99%

“…Recent works like [9] have proposed a method called 'distance-based convolution clustering' (DCC), in which the robot's scanned points are grouped into clusters using a convolution operation. They also proposed a new algorithm to detect lines by appending a cluster of points using a combined Hough transform and line-tracking algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…A similarity index matrix (SIM) using fuzzy membership is used to extract and merge suitable line segments. Meanwhile, in [9], a 'reduced Hough transform line tracker' (REHOLT) is discussed, where voting in the accumulator space in the Hough domain is reduced by first fitting the scan points by a line tracking algorithm and then obtaining a single line using the Hough transform method. It requires tuning the parameters obtained from the line tracking algorithm prior to getting a good estimate of the position of the line.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Algorithms and a Framework for Indoor Robot Mapping in a Noisy Environment Using Clustering in Spatial and Hough Domains

Ravankar

Hoshino

Ravankar

et al. 2015

International Journal of Advanced Robotic Systems

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Map generation by a robot in a cluttered and noisy environment is an important problem in autonomous robot navigation. This paper presents algorithms and a framework to generate 2D line maps from laser range sensor data using clustering in spatial (Euclidean) and Hough domains in noisy environments. The contributions of the paper are: (1) it shows the applicability of density-based clustering methods and mathematical morphological techniques generally used in image processing for noise removal from laser range sensor data; (2) it presents a new algorithm to generate straight-line maps by applying clustering in the spatial domain; (3) it presents a new algorithm for robot mapping using clustering in a Hough domain; and (4) it presents a new framework to load, delete, install or update appropriate kernels in the robot remotely from the server. The framework provides a means to select the most appropriate kernel and fine-tune its parameters remotely from the server based on online feedback, which proves to be very efficient in dynamic environments with noisy conditions. The accuracy and performance of the techniques presented in this paper are discussed with conventional line segment-based EKF-SLAM and the results are compared.

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“…Obtain straight-line maps by applying SVD or Hough extraction of the corners, other techniques like the split and merge [8,9,45] and IEPF [33] algorithms discussed earlier can be used.…”

Section: A Hough Transform Is a Technique For Line Extraction From Bimentioning

confidence: 99%

Section: Clustering In the Hough Domain (Accumulator Space)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Algorithms and a Framework for Indoor Robot Mapping in a Noisy Environment Using Clustering in Spatial and Hough Domains

Ravankar

Hoshino

Ravankar

et al. 2015

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

show abstract

“…This section is based in the revision of existing line extraction algorithms given in [6]. Probably, the most straightforward example of a line extractor is the Successive Edge Following (SEF) algorithm [9].…”

Section: Line Extraction Algorithmsmentioning

confidence: 99%