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Ground Object Recognition using Laser Radar Data: Geometric Fitting, Performance Analysis, and Applications
Linköpings universitet, Institutionen för systemteknik. Linköpings universitet, Tekniska högskolan.ORCID-id: 0000-0002-4434-8055
2006 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Institutionen för systemteknik , 2006.
Serie
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1055
Nyckelord [en]
Laser radar, object detection, object recognition, performance, least squares, ICP, Cramér-Rao
Nationell ämneskategori
Signalbehandling
Identifikatorer
URN: urn:nbn:se:liu:diva-7685ISBN: 91-85643-53-X (tryckt)OAI: oai:DiVA.org:liu-7685DiVA, id: diva2:22679
Disputation
2006-11-17, BL32-Nobel, Campus Valla, Linköpings universitet, Linköping, 13:15 (Engelska)
Opponent
Handledare
Tillgänglig från: 2006-11-06 Skapad: 2006-11-06 Senast uppdaterad: 2016-08-31
Delarbeten
1. Ground Target Recognition using Rectangle Estimation
Öppna denna publikation i ny flik eller fönster >>Ground Target Recognition using Rectangle Estimation
2006 (Engelska)Ingår i: IEEE Transactions on Image Processing, ISSN 1057-7149, E-ISSN 1941-0042, Vol. 15, nr 11, s. 3400-3408Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
IEEE Signal Processing Society, 2006
Nyckelord
Automatic target recognition, Laser radar, Rectangle estimation
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:liu:diva-14120 (URN)10.1109/TIP.2006.881965 (DOI)
Tillgänglig från: 2006-11-06 Skapad: 2006-11-06 Senast uppdaterad: 2017-12-13
2. 3D Content-Based Model Matching using Geometric Features
Öppna denna publikation i ny flik eller fönster >>3D Content-Based Model Matching using Geometric Features
2006 (Engelska)Rapport (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2006. s. 8
Serie
LiTH-ISY-R, ISSN 1400-3902 ; 2726
Nyckelord
Geometric features, Early rejection, Content-based matching, Least squares
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:liu:diva-14121 (URN)LITH-ISY-R-2726 (ISRN)
Tillgänglig från: 2006-11-06 Skapad: 2006-11-06 Senast uppdaterad: 2016-08-31Bibliografiskt granskad
3. Influence of Laser Radar Sensor Parameters on Range Measurement and Shape Fitting Uncertainties
Öppna denna publikation i ny flik eller fönster >>Influence of Laser Radar Sensor Parameters on Range Measurement and Shape Fitting Uncertainties
2007 (Engelska)Ingår i: Optical Engineering: The Journal of SPIE, ISSN 0091-3286, E-ISSN 1560-2303, Vol. 46, nr 10Artikel i tidskrift (Refereegranskat) 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.

Ort, förlag, år, upplaga, sidor
SPIE - International Society for Optical Engineering, 2007
Nyckelord
Range error, Laser radar, Time of flight, Peak detection, Matched filter, Performance
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:liu:diva-14122 (URN)10.1117/1.2789654 (DOI)
Tillgänglig från: 2006-11-06 Skapad: 2006-11-06 Senast uppdaterad: 2017-12-13
4. Performance Analysis of Measurement Error Regression in Direct-Detection Laser Radar Imaging
Öppna denna publikation i ny flik eller fönster >>Performance Analysis of Measurement Error Regression in Direct-Detection Laser Radar Imaging
2003 (Engelska)Ingår i: Proceedings of the 2003 IEEE Conference on Acoustics, Speech and Signal Processing, 2003, Vol. 6, s. 545-548 vol.6Konferensbidrag, Publicerat paper (Refereegranskat)
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.

Nyckelord
Atmospheric turbulence, Electromagnetic wave reflection, Measurement errors, Laser radar, Parameter estimation, Cramer-Rao lower bound
Nationell ämneskategori
Teknik och teknologier Reglerteknik
Identifikatorer
urn:nbn:se:liu:diva-14123 (URN)10.1109/ICASSP.2003.1201739 (DOI)0-7803-7663-3 (ISBN)
Konferens
2003 IEEE Conference on Acoustics, Speech and Signal Processing, Hong Kong, China, April, 2003
Tillgänglig från: 2006-11-06 Skapad: 2006-11-06 Senast uppdaterad: 2016-08-31
5. Ground Target Recognition in a Query-Based Multi-Sensor Information System
Öppna denna publikation i ny flik eller fönster >>Ground Target Recognition in a Query-Based Multi-Sensor Information System
Visa övriga...
2006 (Engelska)Rapport (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Linköping, Sweden: Department of Electrical Engineering, 2006. s. 29
Serie
LiTH-ISY-R, ISSN 1400-3902 ; 2748
Nyckelord
Multi-sensor fusion, Query languages, Infrared sensors, Laser radar, Range data, Target recognition, Target detection
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:liu:diva-14124 (URN)LiTH-ISY-R-2748 (ISRN)
Tillgänglig från: 2006-11-06 Skapad: 2006-11-06 Senast uppdaterad: 2016-08-31Bibliografiskt granskad
6. Approaches to Object/Background Segmentation and Object Dimension Estimation
Öppna denna publikation i ny flik eller fönster >>Approaches to Object/Background Segmentation and Object Dimension Estimation
2006 (Engelska)Rapport (Övrigt vetenskapligt)
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.

Ort, förlag, år, upplaga, sidor
Linköping: Linköping University Electronic Press, 2006. s. 25
Serie
LiTH-ISY-R, ISSN 1400-3902 ; 2746
Nyckelord
Segmentation, Bayes, Rectangle estimation, Least squares, Optimization
Nationell ämneskategori
Reglerteknik
Identifikatorer
urn:nbn:se:liu:diva-14125 (URN)LiTH-ISY-R-2746 (ISRN)
Tillgänglig från: 2006-11-06 Skapad: 2006-11-06 Senast uppdaterad: 2016-08-31Bibliografiskt granskad

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