liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Ground Object Recognition using Laser Radar Data: Geometric Fitting, Performance Analysis, and Applications
Linköping University, Department of Electrical Engineering. Linköping University, The Institute of Technology.ORCID iD: 0000-0002-4434-8055
2006 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis concerns detection and recognition of ground object using data from laser radar systems. Typical ground objects are vehicles and land mines. For these objects, the orientation and articulation are unknown. The objects are placed in natural or urban areas where the background is unstructured and complex. The performance of laser radar systems is analyzed, to achieve models of the uncertainties in laser radar data.

A ground object recognition method is presented. It handles general, noisy 3D point cloud data. The approach is based on the fact that man-made objects on a large scale can be considered be of rectangular shape or can be decomposed to a set of rectangles. Several approaches to rectangle fitting are presented and evaluated in Monte Carlo simulations. There are error-in-variables present and thus, geometric fitting is used. The objects can have parts that are subject to articulation. A modular least squares method with outlier rejection, that can handle articulated objects, is proposed. This method falls within the iterative closest point framework. Recognition when several similar models are available is discussed.

The recognition method is applied in a query-based multi-sensor system. The system covers the process from sensor data to the user interface, i.e., from low level image processing to high level situation analysis.

In object detection and recognition based on laser radar data, the range value’s accuracy is important. A general direct-detection laser radar system applicable for hard-target measurements is modeled. Three time-of-flight estimation algorithms are analyzed; peak detection, constant fraction detection, and matched filter. The statistical distribution of uncertainties in time-of-flight range estimations is determined. The detection performance for various shape conditions and signal-tonoise ratios are analyzed. Those results are used to model the properties of the range estimation error. The detector’s performances are compared with the Cramér-Rao lower bound.

The performance of a tool for synthetic generation of scanning laser radar data is evaluated. In the measurement system model, it is possible to add several design parameters, which makes it possible to test an estimation scheme under different types of system design. A parametric method, based on measurement error regression, that estimates an object’s size and orientation is described. Validations of both the measurement system model and the measurement error model, with respect to the Cramér-Rao lower bound, are presented.

Place, publisher, year, edition, pages
Institutionen för systemteknik , 2006.
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1055
Keyword [en]
Laser radar, object detection, object recognition, performance, least squares, ICP, Cramér-Rao
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:liu:diva-7685ISBN: 91-85643-53-X (print)OAI: oai:DiVA.org:liu-7685DiVA: diva2:22679
Public defence
2006-11-17, BL32-Nobel, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Opponent
Supervisors
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2016-08-31
List of papers
1. Ground Target Recognition using Rectangle Estimation
Open this publication in new window or tab >>Ground Target Recognition using Rectangle Estimation
2006 (English)In: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 15, no 11, 3400-3408 p.Article in journal (Refereed) Published
Abstract [en]

We propose a ground target recognition method based on 3-D laser radar data. The method handles general 3-D scattered data. It is based on the fact that man-made objects of complex shape can be decomposed to a set of rectangles. The ground target recognition method consists of four steps; 3-D size and orientation estimation, target segmentation into parts of approximately rectangular shape, identification of segments that represent the target's functional/main parts, and target matching with CAD models. The core in this approach is rectangle estimation. The performance of the rectangle estimation method is evaluated statistically using Monte Carlo simulations. A case study on tank recognition is shown, where 3-D data from four fundamentally different types of laser radar systems are used. Although the approach is tested on rather few examples, we believe that the approach is promising.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2006
Keyword
Automatic target recognition, Laser radar, Rectangle estimation
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-14120 (URN)10.1109/TIP.2006.881965 (DOI)
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2017-12-13
2. 3D Content-Based Model Matching using Geometric Features
Open this publication in new window or tab >>3D Content-Based Model Matching using Geometric Features
2006 (English)Report (Other academic)
Abstract [en]

We present an approach that utilizes efficient geometric feature extraction and a matching method that takes articulation into account. It is primarily applicable for man-made objects. First the object is analyzed to extract geometric features, dimensions and rotation are estimated and typical parts, so-called functional parts, are identified. Examples of functional parts are a box's lid, a building's chimney, or a battle tank's barrel. We assume a model library with full annotation. The geometric features are matched with the model descriptors, to gain fast and early rejection of non-relevant models. After this pruning the objectis matched with relevant, usually few, library models. We propose a sequential matching, where the number of functional parts increases in each iteration. The division into parts increases the possibility for correct matching result when several similar models are available. The approach is exemplifi…ed with an vehicle recognition application, where some vehicles have functional parts.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2006. 8 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2726
Keyword
Geometric features, Early rejection, Content-based matching, Least squares
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-14121 (URN)LITH-ISY-R-2726 (ISRN)
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2016-08-31Bibliographically approved
3. Influence of Laser Radar Sensor Parameters on Range Measurement and Shape Fitting Uncertainties
Open this publication in new window or tab >>Influence of Laser Radar Sensor Parameters on Range Measurement and Shape Fitting Uncertainties
2007 (English)In: Optical Engineering: The Journal of SPIE, ISSN 0091-3286, E-ISSN 1560-2303, Vol. 46, no 10Article in journal (Refereed) Published
Abstract [en]

Inobject/target reconstruction and recognition based on laser radar data, the range value's accuracy is important. The range data accuracy depends on the accuracy in the laser radar's detector, especially the algorithm used for time-of-flight estimation. In this paper, a general direct-detection laser radar system applicable for hard-target measurements is modeled. The time- and range-dependent laser radar cross sections are derived for some simple geometric shapes (plane, cone, sphere, and paraboloid). The cross-section models are used, in simulations, to find the proper statistical distribution of uncertainties in time-of-flight range estimations. Three time-of-flight estimation algorithms are analyzed: peak detection, constant-fraction detection, and matched filter. The detection performance for various shape conditions and signal-to-noise ratios is analyzed. Two simple shape reconstruction examples are shown, and the detectors' performance is compared with the Cramér-Raolower bound. The performance of the peak detection and the constant-fraction detection is more dependent on the shape and noise level than that of the matched filter. For line fitting the matched filter performs close to the Cramér-Rao lower bound.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2007
Keyword
Range error, Laser radar, Time of flight, Peak detection, Matched filter, Performance
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-14122 (URN)10.1117/1.2789654 (DOI)
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2017-12-13
4. Performance Analysis of Measurement Error Regression in Direct-Detection Laser Radar Imaging
Open this publication in new window or tab >>Performance Analysis of Measurement Error Regression in Direct-Detection Laser Radar Imaging
2003 (English)In: Proceedings of the 2003 IEEE Conference on Acoustics, Speech and Signal Processing, 2003, Vol. 6, 545-548 vol.6 p.Conference paper, Published paper (Refereed)
Abstract [en]

In this paper a tool for synthetic generation of scanning laser radar data is described and its performance is evaluated. By analyzing data from the system, we recognize objects on the ground. In the measurement system it is possible to add several design parameters, which make it possible to test an estimation scheme under different types of system design. The measurement system model includes laser characteristics, object geometry, reflection, speckles, atmospheric attenuation, turbulence and a direct detection receiver. A parametric method that estimates an object's size and orientation is described. There are measurement errors present and thus, the parameter estimation is based on a measurement error model. The parameter estimation accuracy is limited by the Cramer-Rao lower bound. Validations of both the measurement error model and the measurement system are shown. Data from both models generate parameter estimates that are close to the Cramer-Rao lower bound.

Keyword
Atmospheric turbulence, Electromagnetic wave reflection, Measurement errors, Laser radar, Parameter estimation, Cramer-Rao lower bound
National Category
Engineering and Technology Control Engineering
Identifiers
urn:nbn:se:liu:diva-14123 (URN)10.1109/ICASSP.2003.1201739 (DOI)0-7803-7663-3 (ISBN)
Conference
2003 IEEE Conference on Acoustics, Speech and Signal Processing, Hong Kong, China, April, 2003
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2016-08-31
5. Ground Target Recognition in a Query-Based Multi-Sensor Information System
Open this publication in new window or tab >>Ground Target Recognition in a Query-Based Multi-Sensor Information System
Show others...
2006 (English)Report (Other academic)
Abstract [en]

We present a system covering the complete process for automatic ground target recognition, from sensor data to the user interface, i.e., from low level image processing to high level situation analysis. The system is based on a query language and a query processor, and includes target detection, target recognition, data fusion, presentation and situation analysis. This paper focuses on target recognition and its interaction with the query processor. The target recognitionis executed in sensor nodes, each containing a sensor and the corresponding signal/image processing algorithms. New sensors and algorithms are easily added to the system. The processing of sensor data is performed in two steps; attribute estimation and matching. First, several attributes, like orientation and dimensions, are estimated from the (unknown but detected) targets. These estimates are used to select the models of interest in a matching step, where the targetis matched with a number of target models. Several methods and sensor data types are used in both steps, and data is fused after each step. Experiments have been performed using sensor data from laser radar, thermal and visual cameras. Promising results are reported, demonstrating the capabilities of the target recognition algorithms, the advantages of the two-level data fusion and the query-based system.

Place, publisher, year, edition, pages
Linköping, Sweden: Department of Electrical Engineering, 2006. 29 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2748
Keyword
Multi-sensor fusion, Query languages, Infrared sensors, Laser radar, Range data, Target recognition, Target detection
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-14124 (URN)LiTH-ISY-R-2748 (ISRN)
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2016-08-31Bibliographically approved
6. Approaches to Object/Background Segmentation and Object Dimension Estimation
Open this publication in new window or tab >>Approaches to Object/Background Segmentation and Object Dimension Estimation
2006 (English)Report (Other academic)
Abstract [en]

In this paper, optimization approaches for object/background segmentation and object dimension/orientation estimation are studied. The data sets are collected with a laser radar or are simulated laser radar data. Three cases are defined: 1) Segmentation of the data set into object and background data. When there are several objects present in the scene, data from each object is also separated into different clusters. Bayesian hypothesis testing of two classes is studied. 2) Estimation of the object’s dimensions and orientation using object data only. 3) Estimation of the object’s dimensions and orientation using both object and background data. The dimension and orientation estimation problem is formulated using non-convex optimization, least squares and, convex optimization expressions. The performance of the methods are investigated in simulations.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2006. 25 p.
Series
LiTH-ISY-R, ISSN 1400-3902 ; 2746
Keyword
Segmentation, Bayes, Rectangle estimation, Least squares, Optimization
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-14125 (URN)LiTH-ISY-R-2746 (ISRN)
Available from: 2006-11-06 Created: 2006-11-06 Last updated: 2016-08-31Bibliographically approved

Open Access in DiVA

fulltext(3542 kB)7903 downloads
File information
File name FULLTEXT01.pdfFile size 3542 kBChecksum MD5
6b5c780b30268f370544c44c41c79e7c89937372b701ba3c552d6b6780741543c4844dc0
Type fulltextMimetype application/pdf

Authority records BETA

Grönwall, Christna

Search in DiVA

By author/editor
Grönwall, Christna
By organisation
Department of Electrical EngineeringThe Institute of Technology
Signal Processing

Search outside of DiVA

GoogleGoogle Scholar
Total: 7903 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

isbn
urn-nbn

Altmetric score

isbn
urn-nbn
Total: 3665 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf