Photogrammetric features for the registration of ... · teknoloji ile sağlanmaktadır: fotogrametri ve lazer taraması. Lazer taramalarının toplanması ve islenmesin-de zaman kaybı

© 2013 HKMO

UCTEAChamber of Surveying and Cadastre Engineers

Journal of Geodesy and Geoinformation

TMMOBHarita ve Kadastro Mühendisleri Odası

Jeodezi ve Jeoinformasyon Dergisi

Vol.2 Iss.1 pp.1-8 May 2013 Journal No.107 Doi: 10.9733/jgg.250913.2 www.hkmodergi.org

Photogrammetric features for the registration of terrestrial laser scans with minimum overlapSibel Canaz1,2,* Ayman Habib1

1Department of Geomatics Engineering, University of Calgary, Schulich School of Engineering, Calgary, AB, Canada 2Deparment of Geomatics Engineering, Karadeniz Technical University, Trabzon, Turkey

Volume: 2Issue: 1Page: 1 - 8May 2013

AbstractThe interest and demand for 3 Dimensional (3D) documentation of man-made structures have increased with the continuous improvement in data acquisition systems and the expanding range of potential applications. Currently, 3D data can be obtained through two technologies: photogrammetry and laser scanning. In spite of the proven quality of static laser scanning, the collection and processing of laser scans is a time consum-ing process and the derivation of complete 3D models requires multiple scans with each scan having its own coordinate system. Therefore, the different scans should be aligned in a common coordinate system. This alignment process is known as “registration”. Current registration techniques require large overlap area between the collected scans in order to obtain reliable estimation of the transformation parameters relating these scans. The main objective of this research is to avoid the requirement of large overlap areas among the laser scans using photogrammetric data for the registration process. Registration methods generally use points as primitives to relate the different laser scans. In this research, photogrammetric linear and planar features are used for the proposed registration method. Finally, qualitative and quantitative quality control measures are proposed for analyzing the result of the proposed registration method.

KeywordsTerrestrial laser scanning, Close range photogrammetry, Registration, Feature extraction

Cilt: 2Sayı: 1Sayfa: 1 - 8Mayıs 2013

ÖzetMinimum çakışan yersel lazer taramalarının birleştirmesi için fotogrametrik özelliklerGünümüzde, insan yapımı binaların 3 Boyutlu (3B) modellemesine olan ilgi ve talep veri toplama sistemle-rindeki gelişmeler ve büyüyen potansiyel uygulama aralığı ile birlikte arttı. Mevcut durumda, 3B veriler iki teknoloji ile sağlanmaktadır: fotogrametri ve lazer taraması. Lazer taramalarının toplanması ve islenmesin-de zaman kaybı söz konusudur. Bütün bir 3B model elde etmek için birçok lazer taraması gerekmektedir. Her bir lazer taraması ayrı koordinat sistemlerinde olduğu için; 3B modeller ancak bu farklı koordinatlardaki lazer tamamlarının bir araya dizilmesi ile mümkündür. Bu dizilim “birleştirme” olarak bilinir. Birçok bir-leştirme teknikleri doğru 3B model için toplanan lazer taramaları arasında büyük oranda çakışma ister. Bu araştırmanın temel amacı fotogrametrik veri kullanarak lazer taramaları arasındaki büyük oranda istenilen çakışmadan kaçınmaktır. Birleştirme tekniklerinde genellikle noktalar kullanarak lazer taramaları arasın-daki ilişki bulunur. Bu önerilen araştırmada düzlemsel ve doğrusal özellikler çıkarılmış ve kullanılmıştır. Son olarak, nitel ve nicel kalite kontrol yöntemleri önerilen birleştirme metodu sonucunu analiz etmek için yapılmıştır.

Anahtar SözcüklerYersel lazer tarayıcı, Yersel fotogrametri, Birleştirme, Özellik çıkarma

*Corresponding Author: Tel: +90 462 377 27 76 Fax: +90 462 328 0918E-mail: [email protected] (Canaz S.), [email protected] (Habib A.)

mailto:[email protected]

mailto:[email protected]

2 Photogrammetric features for the registration of terrestrial laser scans with minimum overlap

1. IntroductionRepresentation of an object in a digital environment is denot-ed as 3D modeling. 3D models of man-made structures are necessary for their digital documentation, which is useful for a wide range of applications. For instance, 3D models of his-torical buildings can help in their reconstruction if they are damaged by natural or human events. Another 3D modeling application can be given in archeology where sites/objects can be studied through an investigation of their 3D models. Some other areas that frequently use 3D models include ar-chitecture, forestry, geology, urban planning, and civil engi-neering. Due to the wide spectrum of applications that are based on 3D models, several researchers have been focusing on creating 3D models of our environment in general and man-made structures in particular.

Laser scanning technology has become an established technique for creating 3D models due to their ability of direct acquisition of 3D data. A more traditional approach for deriving 3D models of objects of interest is photogram-metric reconstruction. In contrast to laser scanning, 3D data can be only derived from overlapping imagery whenever conjugate features have been identified and the intersection of the respective spatial rays is mathematically described. Considering the characteristics of laser scanning and pho-togrammetric systems, the user community has been inter-ested in utilizing the former for 3D model generation due to the direct derivation of the coordinates of scanned sur-faces. However, in many applications, it is almost impos-sible to cover a complete structure with a single Terrestrial Laser Scanner (TLS) scan. Therefore, several TLS scans from different stations are necessary for complete coverage of the surveyed structure (i.e., different scans from different stations produce sub-models of the object in question). The captured scans/sub-models are related to different coordinate systems associated with the laser scanning units at the differ-ent stations. To obtain meaningful 3D models, alignment of the collected scans in a common coordinate system is a pre-requisite. This alignment process is known as registration.

An effective registration paradigm has four components that should be carefully established: appropriate primitives, transformation parameters, similarity measure, and match-ing strategy (Al-Ruzouq 2004). First of all, registration primitives, which can be reliably identified/extracted from the overlapping sub-models, should be established. The used primitives for the registration of TLS scans can be either point, linear, or planar (areal) features. Identification of con-jugate points in different TLS scans is almost impossible due to the irregular distribution of the TLS point cloud. On the other hand, it is possible to identify planar and linear features in TLS scans (Habib et al. 2008). For this reason, planar and linear features are chosen as the registration primitives in this research. The second element of a registration paradigm is establishing the transformation parameters, which describe the relationships between the reference frames of the in-volved datasets (i.e., sub-models covering different portions of the object in question). Usually, three translations, three rotations, and a scale factor are used to define the relation-ship between the reference frames of the sub-models. The third element of the registration paradigm, which is the sim-ilarity measure, incorporates the matched primitives in over-

lapping scans together with the transformation parameters to mathematically describe the coincidence of the involved primitives following the registration process (Renaduin et al. 2011). Finally, the matching strategy, which can be consid-ered as an overall scheme for solving the registration prob-lem, needs to be designed to manipulate the primitives for the estimation of the necessary transformation parameters using the established similarity measure (Al-Ruzouq 2004).

There are many different registration techniques avail-able in the literature. The Iterative Closest Point (ICP), which is based on minimizing the point-to-point distance in the overlapping area between different TLS scans, is the most popular and generally used algorithm for registering 3D point clouds (Besl and McKay 1992). Basically, the sum of squares of the Euclidean distances between the nearest points of two data sets is minimized in the ICP registration technique. Similar to the ICP, Chen and Medioni (1992) de-veloped a method, where the point-to-plane normal distances in the overlapping area among the TLS scans are minimized. A variant of these methods, the Iterative Closest Projected Point (ICPP) can be considered as both a point-to-point and point-to-plane registration technique (Al-Durgham et al. 2011). The ICPP minimizes the distance between a point in one scan and its projection on the closest three points in the other scan. Some other researchers used a camera that was mounted on the top of the TLS system (a hybrid sys-tem) to integrate photogrammetric data and TLS scans for registration purposes. The collected images were used for registering the TLS scans using the estimated mounting parameters between the two systems. These mounting pa-rameters were determined from a prior system calibration process. Dold and Brenner (2006) conducted a registration study using a hybrid system. Al-Manasir and Fraser (2006) established another example of registration using a hybrid system. Coded targets on the object were used to register TLS scans automatically. The major disadvantage of using a hybrid system for registration purposes is that it is not al-ways possible to have a good observation station for col-lecting both a TLS scan and photogrammetric data at the same time. Global Positioning Systems (GPS) device can also be used to estimate relations between TLS scans and reference coordinate system while collecting the scans. For instance Balzani et al. (2002) conducted a study for geo-ref-erencing scans using GPS device. The drawback behind this methodology for registering scans is that the effort in using GPS to collect control points for the geo-referencing of the scans that have minimum overlap is more than the required effort in the image data acquisition. The ICP and many oth-er registration methods generally require large overlap areas among the TLS scans for deriving reliable estimates of the transformation parameters relating the different sub-models. In this research, the large overlap area requirement among the scans is eliminated/reduced using photogrammetric data, which can be acquired in a relatively short time. Since linear and planar features can be easily derived from the photo-grammetric data, identified linear and planar features in both the photogrammetric model and the TLS scans are used for the co-alignment of different scans to a common reference frame.

Overall, there are many advantages of the proposed reg-istration method. The main advantage of having minimum

3Sibel Canaz, Ayman Habib / Vol.2 Iss.1 2013

overlap area among the TLS scans and using photogrammet-ric data to acquire 3D model is that the proposed procedure will lead to reduction in the field data collection time, which will lead to reduction in the acquisition cost. Moreover, in the presence of some restrictions during the data acquisition (e.g., busy traffic, cluttered environment, and/or unstable soil), fewer scans would be needed to cover the object of interest. On the other hand, the image data acquisition can be done quickly with minimum time and site stability re-quirements. Furthermore, the proposed procedure will lead to reduction in the amount of collected data, which will have a positive impact on the data processing and manip-ulation activities. In this regard, it should be noted that the massive amount of collected data by terrestrial laser scans would increase the complexity and storage requirements for the subsequent data processing activities. In addition, without image data, significant overlap among the scans as well as the presence of variation in the slopes/aspects within the overlap regions are necessary for the registration these scans. In some situations, this would not be possible. For example, if we have a structure with a large extent that does not exhibit good geometry in the overlap regions (i.e., the overlap region regardless of its extent would not have good geometry for reliable registration – e.g., building with a large facade). In those situations, the proposed procedure would facilitate the possibility of registering these scans as long as the scan has a minimum of two non-parallel linear fea-tures that could be identified in the photogrammetric model. Another advantage of the proposed procedure is that it will ensure the co-registration of multi-sensory data to a common platform. One of the main benefits of such a registration is the straightforward ability of linking the spectral information from imagery with the laser scanning point cloud. Moreover, the same procedure can be used to for the alignment of ter-restrial laser scans with minimal overlap with airborne laser scanning data or airborne imagery that have common fea-tures that could be identified in the TLS scans. Finally, with the increasing interest in using overlapping imagery together with dense matching techniques, the possibility of generat-ing a dense point cloud from imagery is becoming a reality. The proposed procedure would allow for the registration of the laser and image-based point clouds. Such a registration would have a positive impact on the calibration of laser scan-ning systems as well as the refinement of the dense matching algorithms and its results.

Following the registration process, a quality control pro-cedure is then adopted to ensure/verify the quality of the estimated transformation parameters. A qualitative quality control is conducted by plotting and visually inspecting the registered scans. A quantitative quality control, on the other hand, is established by calculating the point-to-plane normal distances between the registered surfaces. As an additional quantitative quality control, results from the proposed reg-istration method are compared with those derived from the ICPP method. The next section deals with the extraction of the registration primitives from the photogrammetric data and the TLS scans. Then, the registration and the quality control process are explained. The experimental results sec-tion evaluates the performance of the proposed procedure. Finally, conclusions and recommendations for future work are presented.

2. Feature extraction from TLS scans and photogrammetric data

2.1 Feature extraction from photogrammetric dataIn this research, we start by collecting a set of overlapping images that cover the object of interest. Following the cam-era calibration and the acquisition of the images that cover the object in question, a set of tie points are identified in the captured imagery to establish the position and orientation of these images in an arbitrarily-defined coordinate system using a bundle adjustment procedure. For the alignment of the TLS scans, additional linear and planar features are in-corporated in the bundle adjustment. Those features will be later identified in the TLS scans. A planar surface is defined by some points along its boundary. More specifically, three or four points are observed in multiple images, and their model space coordinates are estimated through the bundle adjustment procedure. On the other hand, linear features are represented by their end points. The coordinates of the end points are derived from a coplanarity constraint, which can be added to the bundle adjustment procedure (Habib et al. 2004; Habib et al. 2007; and Renaudin et al. 2011). The end points are only identified in one of the images while a set of intermediate points, which are mono-scopically identified in the overlapping images, are used in the coplanarity con-straint. Interested readers can refer to Habib et al. (2004), Habib et al. (2007), and Renaudin et al. (2011) for more de-tails regarding the derivation of the linear features from the photogrammetric data. In summary, a set of points represent-ing planar and linear features will be derived from the pho-togrammetric bundle adjustment procedure. These points are denoted as Photogrammetrically Reconstructed Data (PRD) in this research. The PRD are used together with the extract-ed features from the TLS scans for their registration.

2.2 Feature extraction from terrestrial laser scansFor the feature extraction from the TLS scans, a segmenta-tion procedure is utilized to identify the points that belong to the same plane. In this research, a novel parameter-domain segmentation approach, which is introduced by Lari et al. (2011), is used. In this approach, the neighborhood of each point is established using an adaptive cylinder, whose axis is changing its orientation to be aligned along the normal to the local planar surface through the point in question. The adap-tive cylinder is used to derive the attributes of the point in question. These attributes are stored in an accumulator array. Rather than using a tessellation for the manipulation the ac-cumulator array, a kd-data structure is implemented to store the attributes of the point cloud. Finally, an efficient peak detection procedure is utilized to identify clusters of similar attributes (Lari and Habib 2012). Following the identifica-tion of the planar regions, three or four points from each of the planes that have been identified in the photogrammetric data are manually chosen. It is worth mentioning that the chosen points in the TLS scans are not necessarily conjugate to the selected points in photogrammetric data to define the same planar patch. For the linear feature extraction, neigh-boring planes are intersected. The intersection of such planes provides infinite linear features. The end points of the TLS


linear features are established through the projection of the point cloud in the neighboring planes within a giv-en buffer around their intersection onto the infinite linear feature (Al-Durgham 2007) Similar to the planar features, the points defining the lines in the photogrammetric and TLS data need not be conjugate. The sets of non-conjugate points along corresponding linear and planar surfaces are used for the registration process. One should note that at this stage, the photogrammetric model generation, including the deri-vation of linear and planar features, is based on a manual processing of the imagery. The automated triangulation and derivation of linear features are the focus of current and fu-ture research. However, the linear and planar features are automatically extracted from the TLS data. The matching of image and TLS linear features are based on manual proce-dures. Examples of automatically derived linear and planar features from a TLS scan and manually extracted features from the photogrammetric data are shown in Figure 1.

3. Similarity measure and quality controlTo estimate the transformation parameters between the pho-togrammetrically-reconstructed data (PRD), the TLS scans, and the ground coordinate system, a similarity measure is used. Since planar and linear features are represented by points, a point-based 3D similarity transformation is cho-sen for relating the observed conjugate features in the PRD, the TLS scans, and the ground coordinate system (Equation 1). One should note that a 3D similarity transformation is commensurate with the transformation parameters (i.e., three shifts, three rotations, and a scale factor) relating the reference frames of the involved data. Although that there is no scale difference among the TLS data and that its scale is correctly set by the measured ranges, the scale is used in this model to allow for the scaling of the photogrammetric model to fit the TLS data since the scale is arbitrarily established in the 3D model derivation process from imagery.

Figure 1: Examples of automatically derived linear and planar features from a TLS scan (a) and the manually extracted corresponding features in a photogrammetric data (b)

As mentioned earlier, the points representing corresponding features from the photogrammetric model and the TLS scans are not necessarily conjugate. Therefore, the traditional LSA procedure is modified by changing the weights of the error vector associated with the observation equations (Equation 1). The conceptual basis of the weight modification is to min-imize the normal distances between conjugate features rather than minimizing the distances between non-conjugate points


linear features are established through the projection of the point cloud in the neighboring planes within a giv-en buffer around their intersection onto the infinite linear feature (Lari and Habib 2012) Similar to the planar features, the points defining the lines in the photogrammetric and TLS data need not be conjugate. The sets of non-conjugate points along corresponding linear and planar surfaces are used for the registration process. One should note that at this stage, the photogrammetric model generation, including the deri-vation of linear and planar features, is based on a manual processing of the imagery. The automated triangulation and derivation of linear features are the focus of current and fu-ture research. However, the linear and planar features are automatically extracted from the TLS data. The matching of image and TLS linear features are based on manual proce-dures. Examples of automatically derived linear and planar features from a TLS scan and manually extracted features from the photogrammetric data are shown in Figure 1.





linear features are established through the projection of the point cloud in the neighboring planes within a giv-en buffer around their intersection onto the infinite linear feature (Lari and Habib 2012) Similar to the planar features, the points defining the lines in the photogrammetric and TLS data need not be conjugate. The sets of non-conjugate points along corresponding linear and planar surfaces are used for the registration process. One should note that at this stage, the photogrammetric model generation, including the deri-vation of linear and planar features, is based on a manual processing of the imagery. The automated triangulation and derivation of linear features are the focus of current and fu-ture research. However, the linear and planar features are automatically extracted from the TLS data. The matching of image and TLS linear features are based on manual proce-dures. Examples of automatically derived linear and planar features from a TLS scan and manually extracted features from the photogrammetric data are shown in Figure 1.



(1)

where:

- Scani indicates the ith TLS scan;

- [

] is the observation vector of the point coordinates either in the ith TLS scan or the

photogrammetrically reconstructed data;

- [

] is the translation vector between either the ith TLS scan or the

photogrammetrically reconstructed data and the ground coordinate systems;

- is the scale factor between either the ith TLS scan or the photogrammetrically reconstructed data

and the ground coordinate systems;

- ⌊

⌋ is the rotation matrix relating either the ith TLS scan or the

photogrammetrically reconstructed data and the ground coordinate systems, defined by the angles Ω, Ф,

and Κ; and

- [

] is the ground coordinates of the corresponding point in the object space.

(1)

where:


- [



- [





- ⌊



and Κ; and

- [


(1)

where:


- [



- [





- ⌊



and Κ; and

- [



(1)

where:


- [



- [





- ⌊



and Κ; and

- [


- Xscani /PRD) =

is the observation vector of the point coordinates either in the ith TLS scan or the photogrammetrically re-constructed data;

(1)

where:


- [



- [





- ⌊



and Κ; and

- [


- XT scani /PRD) =

is the translation vector between either the ith TLS scan or the pho-togrammetrically reconstructed data and the ground coordinate systems;

(1)

where:


- [



- [





- ⌊



and Κ; and

- [


- R

scani /PRD) =

is the rotation matrix relating ei-ther the ith TLS scan or the pho-togrammetrically reconstructed data and the ground coordinate systems, defined by the angles Ω, Ф, and Κ; and

(1)

where:


- [



- [





- ⌊



and Κ; and

- [


- XG = is the ground coordinates of the corresponding point in the object space.

- S

scani /PRD) is the scale factor between either the ith TLS scan or the photogrammetrically reconstructed data and the ground coordinate systems;



along corresponding linear or planar features. The modifi-cation process is established by assigning zero weights to points along the directions of the enclosing linear or planar feature. Thus, only the weights in the normal direction to the linear or planar feature are considered in the least squares minimization process. Therefore, the transformation param-eters relating the different scans and the photogrammetric model are estimated to minimize the normal distances be-tween conjugate features in the sub-models. For more details regarding the weight modification process, interested read-ers can refer to Renaduin et al. (2011), Habib et al. (2011), and Kersting (2012). Since the photogrammetric features are referenced to a unique reference frame (i.e., the arbitrari-ly-chosen reference frame for the bundle adjustment), the registration process will implicitly align the different TLS scans to the photogrammetric model to form a single model in the photogrammetric reference frame while simultaneous-ly scaling the photogrammetric model to fit the TLS data. Then, the augmented model is related to the ground coordi-nate system through the provided control. In this research, the reference frame for one of the TLS scans, which is denot-ed as the reference scan, is chosen as the ground coordinate system. Therefore, the least squares adjustment process will align the different scans and the photogrammetric model un-til they fit as well as possible with the corresponding features in the reference scan.

To test the feasibility of the proposed procedure, the reg-istered scans are qualitatively and quantitatively evaluated. Qualitative quality control is conducted by transforming all of the TLS scans to the reference coordinate system using the estimated transformation parameters and plotting them together. Then, the quality of registration is evaluated by vi-sually checking the closeness of the point clouds from the different scans in the overlap area. In addition, quantitative quality control is performed using either one of the follow-ing approaches. First, the point-to-plane normal distances among conjugate planar patches are evaluated and analyzed. The point-to-plane normal distances are evaluated for over-lapping regions between the PRD and TLS scans and/or overlapping regions between the TLS scans, if available. In the second quantitative quality control approach, the estimat-ed registration results are compared with those derived from the ICPP registration, which is a variant of the ICP approach. It is worth mentioning that additional TLS scans are used for the ICPP registration since it requires large overlap areas among the involved scans. The next section deals with the conducted experimental results to highlight the capabilities of the proposed procedure as well as the comparative perfor-mance of planar and linear features as registration primitives.

4. Experimental resultsIn order to analyze the ability of the proposed registration methods, real experimental data have been collected and

the results of the registration methods are presented. Four minimally overlapping TLS scans were collected around the Rozsa Center at the University of Calgary using the Trimble GS200 scanner, whose maximum range and angular resolu-tion are 200 m and 32 milli-radians (or 3 mm at 100 m), re-spectively. As mentioned before, the ICPP-based registration requires large overlap area among the TLS scans. Therefore, two additional TLS scans and an airborne scan, which have large overlap areas with the other minimum overlapping four scans, were added for the ICPP registration approach. For the photogrammetric reconstruction, sixteen images of the Rozsa Center are collected using a Canon EOS Rebel XS camera with a 0.00571 mm pixel size. The camera has an array dimension of 3888×2592 pixels and an 18 mm nominal focal length.

4.1 Planar feature-based registrationFor this experiment, 30 planar features within the Rozsa Center are identified and extracted/derived from the TLS scans and PRD. These features are used for the proposed registration method, whose results are listed in Table 1. The values between the parentheses are the standard deviation of the estimated parameters. One should note that the co-ordinate system of TLS scan 1 has been used as the ground (reference) coordinate system.

4.2 Linear feature-based registration

For this experiment, 20 different linear features within the Rozsa Center are derived from the TLS scans and PRD, and they are used for the proposed registration method. The es-timated transformation parameters together with their stan-dard deviations from this experiment are shown in Table 2. Comparing the estimated parameters and their standard deviations to those in Table 1, one can hypothesize that the linear features did not provide a registration quality of the same level as the planar features. As can be seen in Table 2, the standard deviation of the vertical displacement for TLS 3 and TLS 4 as well as the Ω and Ф rotation angles for TLS 2, TLS 3, and TLS 4 are significantly higher than those in Table 1 (refer to the highlighted cells in Table 2). The cause of the inferiority of the linear feature-based registration will be discussed in the quality control section of the experimen-tal results.

4.3 Quality control and comparison of the proposed planar and linear registration methodsThe quality control of the proposed linear and planar fea-ture-based registration procedures are conducted through both qualitative and quantitative inspection of the co-aligned scans. For the qualitative quality control, the registered TLS

XT (m) YT (m) ZT (m) Scale Ω (o) Ф (o) Κ (o)

TLS scan1 0 0 0 1 0 0 0

TLS scan2

-23.186(±0.0287)

-14.801(±0.0219)

-0.687(±0.0805) 1 0.234

(±0.1133)-0.429

(±0.2092)8.373

(±0.0484)

TLS scan3

69.677(±0.0221)

92.511 (±0.0293)

1.335 (±0.0431)

1 0.243(±0.0624)

0.313(±0.0781)

121.373(±0.0441)

TLS scan4

-41.693 (±0.0298)

91.370 (±0.0234)

-0.251 (±0.0916) 1 -0.291

(±0.1273)0.165

(±0.0769)-145.531(±0.0447)

PRD 5.372(±0.0184)

1.610 (±0.0163)

37.383 (±0.0143)

0.998 (±0.000)

30.584(±0.2271)

-74.546 (±0.0419)

91.168(±0.2261)

XT (m) YT (m) ZT (m) Scale Ω (o) Ф (o) Κ (o)

TLS scan1 0 0 0 1 0 0 0

TLS scan2

-23.217 (±0.0685)

-14.792 (±0.0341)

-0.667 (±0.1226) 1 0.869

(±0.2571)1.073

(±0.5158)8.421

(±0.1569)

TLS scan3

69.639 (±0.0661)

92.565 (±0.0646)

1.284 (±0.7231)

1 0.681 (±0.4161)

0.824 (±0.5422)

121.411 (±0.1071)

TLS scan4

-41.787 (±0.0983)

91.305 (±0.0724)

-0.751 (±0.3747) 1 -0.803

(±0.4175)0.009

(±0.2541)-145.441 (±0.1263)

PRD 5.461 (±0.0722)

1.635 (±0.0393)

37.384 (±0.0336)

0.998 (±0.0006)

31.338 (±0.8272)

-74.372 (±0.1408)

91.914 (±0.7643)

Table 1: Results of the planar feature-based registration procedure Table 2: Results of the linear feature-based registration procedure


scans are plotted and a visual inspection is conducted in the overlap area. Figure 2 illustrates a portion of the registered scans from both procedures. From that figure, it is quite evident that the planar feature-based registration is show-ing better quality (Figure 2a) while the linear feature-based registration exhibits a problem in the vertical alignment of the different scans (Figure 2b); please refer to the zoomed-in portion corresponding to the lightening rod on top of the building rooftop, where the involved three scans are properly aligned in Figure 2a while a misalignment in the Z-direction is visible in Figure 2b. Such misalignment can be either at-tributed to improperly estimated displacement in the Z di-rection or the Ω and Ф rotation angles relating these scans from the linear feature-based registration. This observation is in agreement with the large standard deviations in Table 2 (please, refer to the highlighted cells in this table).

Quantitative quality control is performed by calculating the point-to-plane normal distances for the planes between the PRD and the TLS scans (i.e., quantitatively showing the compatibility of the photogrammetric and TLS data follow-ing the registration process). Using the estimated transfor-mation parameters, the PRD and the TLS scans are trans-formed into the reference frame coordinate system. For the point-to-plane normal distance estimation, which is the first

quantitative quality control procedure, the plane parameters are determined from the PRD and the normal distances from the individual points in the segmented TLS scans to the PRD plane are then estimated. Finally, a visual illustration of the calculated point-to-plane normal distances is presented as can be seen in Figures 3 and 4. For the planar feature-based registration, the average normal distance turned out to be at the 10 cm level. On the other hand, the linear feature-based registration exhibited an average normal distance of up to 20 cm for horizontal and sloping planar features (i.e., building rooftops in Figure 3). It is worth noting that these planes are the ones that verify the quality of the estimated ZT shift as well as the Ω and Ф rotation angles between the different scans. Once again, these parameters are the ones that exhibit large standard deviations as can be seen in Table 2. The rea-son behind this problem will be explained in the next para-graph. For the second quantitative quality control procedure, a comparison of the results from the proposed registration with those derived from the ICPP is presented in Table 3, where large differences between the results from the differ-ent approaches are highlighted in yellow. Once again, the parameters that are showing the largest deviations are ZT shift as well as the Ω and Ф rotation angles for the linear feature-based registration.

Figure 2: Registered scans using the planar feature-based (a) and linear feature-based registration approaches (b)

Figure 3: Point-to-plane normal distances using the linear feature-based registration method (color is based on normal distances as seen in the scale bar on the right)

Figure 4: Point-to-plane normal distances using the planar feature-based registration method (color is based on normal distances as seen in the scale bar on the right)


Further investigation of the problematic linear feature-based registration revealed the cause of observed anomalies in Figures 2 and 3 as well as Tables 2 and 3. As mentioned earli-er, the registration approach assumes that the derived features from both the photogrammetric data and the TLS scans are conjugate. While this assumption is true for the planar fea-tures, it is not necessarily valid for the linear features. More specifically, the extracted linear features from the TLS scans might not correspond to physical linear features that could be

identified in the photogrammetric data. Figure 5 illustrates an instance of such a problem. This type of problem generally occurs in the roof portion of the scan, which commonly has sloping or horizontal linear features. The non-corresponding sloping or horizontal linear features from the PRD and the TLS scans are the main cause for the incorrect estimation of ZT, Ω, and Ф since these features are the ones contributing to the estimation of these parameters in the least squares ad-justment procedure.

Figure 5: Problem with the linear feature-based registration approach (i.e., TLS linear features that do not correspond to those identifiable in the photogrammetric data)

Registration Parameters

ICPP Method

Planar feature-based method

Linear feature-based method

ICPP vs. Planar feature-based

methods

ICPP vs. linear feature-based

methods

Linear vs. planar

TLS scan2

XT (m) -23.258 -23.186 -23.217 -0.072 -0.041 0.031YT (m) -14.733 -14.801 -14.792 0.067 0.058 -0.009ZT (m) -0.605 -0.687 -0.667 0.082 0.062 -0.020

Scale 1 1 1 0 0 0Ω (o) 0.177 0.234 0.869 -0.056 -0.691 -0.635ф (o) -0.189 -0.429 1.073 0.239 -1.262 -1.502Κ (o) 8.168 8.373 8.421 -0.204 -0.252 -0.048

TLS scan3

XT (m) 69.613 69.677 69.639 -0.063 -0.025 0.038YT (m) 92.579 92.511 92.565 0.068 0.014 -0.054ZT (m) 1.375 1.335 1.284 0.040 0.091 0.051

Scale 1 1 1 0 0 0Ω(o) 0.095 0.243 0.681 -0.147 -0.585 -0.438ф(o) 0.194 0.313 0.824 -0.118 -0.629 -0.511Κ(o) 121.388 121.373 121.411 0.015 -0.022 -0.038

TLS scan4

XT (m) -41.751 -41.693 -41.787 -0.057 0.036 0.094YT (m) 91.313 91.37 91.305 -0.056 0.008 0.065ZT (m) -0.259 -0.251 -0.751 -0.008 0.491 0.500

Scale 1 1 1 0 0 0Ω(o) -0.204 -0.291 -0.803 0.086 0.598 0.512ф(o) 0.002 0.165 0.009 -0.162 -0.006 0.156Κ(o) -145.53 -145.531 -145.441 -0.007 -0.097 -0.09

PRD

XT (m) - 5.372 5.461 - - -0.089YT (m) - 1.611 1.635 - - -0.025ZT (m) - 37.383 37.384 - - -0.001

Scale - 0.998 0.998 - - 0Ω(o) - 30.584 31.338 - - -0.754ф(o) - -74.546 -74.372 - - -0.174Κ(o) - 91.168 91.914 - - -0.746

Table 3: Comparisons of the results from the ICPP and the proposed registration methods


5. Conclusions and recommendations for future workIn this research, a new registration method for the co-align-ment of TLS scans is proposed. While commonly used registration methods, such as the ICP and its variants, fail to register TLS scans with minimum overlap area, the pro-posed registration method has succeeded in registering the scans using photogrammetric data, which can be quickly and economically collected, covering the object of interest. The proposed registration method uses linear or planar features, which have been extracted from photogrammetric and TLS data for the estimation of the transformation parameters relat-ing the different scans. In spite of the fact that non-conjugate points have been used to represent corresponding linear and planar features, a point-based similarity measure has been used after modifying the weights of the random errors in the observation equations to eliminate the systematic component resulting from the use of non-conjugate points and to mini-mize the normal distances between corresponding features in the TLS scans and photogrammetric data.

Qualitative visualization of the registered scans has been used to verify the performance of the proposed approaches. In addition, quantitative quality control measures have been introduced to evaluate the performance of the proposed reg-istration. First, point-to-plane normal distances between the registered surfaces are calculated. Second, we carried out a quantitative comparison between the results from the pro-posed registration approaches and the ICPP method after add-ing more scans to increase the overlap between the involved sub-models. According to the quality control analysis, the planar feature-based registration method gives more reliable results than those derived from the linear feature-based regis-tration. The reason for the inferior performance of the linear features is that extracted linear features from the TLS data do not necessarily correspond to physical linear features that could be identified in the photogrammetric data. Current and future research will be focusing on the automated extraction of the registration primitives from the photogrammetric data. In addition, the automatic identification of conjugate features in both photogrammetric and TLS data will be addressed.

Acknowledgement The Authors would like to acknowledge the financial support of the Natural Science and Engineering Research Council of Canada and TECTERRA. In addition, the help of the mem-bers of the Digital Photogrammetry Research Group (DPRG) at the University of Calgary in the photogrammetric and laser scanning data acquisition is highly appreciated.

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