Relative Orientation Parameters Research Articles

Simultaneous Calibration of Multiple Cameras and Generation of Omnidirectional Images

Abstract. Omnidirectional images are increasingly being used in various areas, such as urban mapping, virtual reality, agriculture, and robotics. These images can be generated by different acquisition systems, including multi-camera systems, which can acquire higher-resolution images. Stitching techniques are often used and can be suitable for non-metric applications, but rigorous photogrammetric processing is recommended when having more accurate requirements. The main challenges related to this kind of product are the system calibration and the generation of the final omnidirectional images. When using multi-camera systems, the displacement of the cameras' perspective centres can affect the generation of the omnidirectional images and the resulting accuracy. A common approach to minimising the resulting parallax error is to establish a value for the projection cylinder radius as close as possible to the object's depth. This work proposes a highly accurate simultaneous calibration technique for multiple camera systems using self-calibrating bundle adjustment with constraints of stability of the relative orientation parameters. These parameters are later used to generate a projecting cylindrical surface, maintaining the original camera perspective centres and relative orientation angles. The experiments show that using constraints improved both the calibration results and the final omnidirectional images. Residual mismatches between points in overlapping areas are subpixel.

DESIGN AND IMPLEMENTATION OF STEREO VISUAL ODOMETRY SYSTEM BASED ON ROP ADJUSTMENT

Abstract. Stereo Visual Odometry (SVO) is a technique used to estimate the continuous position and orientation of a moving platform using a dual-camera system that captures stereo image pairs. To obtain accurate results, A SVO system must be calibrated before use. System calibration is necessary to determine the intrinsic camera parameters (ICPs) and the relative orientation parameters (ROPs) between the cameras at real scale. Compared to monocular visual odometry, a calibrated SVO system can recover the real scale of the translation vector without additional sensors.The proposed method in this study utilizes ROP adjustment for SVO. Instead of conventional bundle adjustment, this method adopts all sets of ROPs as measurements in the designed network adjustment model. Specifically, there are six sets of ROPs among time-adjacent stereo image pairs.A SVO system was designed to implement the proposed SVO method. Two experiments were conducted in outdoor and indoor test fields to evaluate the performance. Several ground check points were set up for distance and position verification. The drift ratio was also evaluated. The results demonstrate that the designed SVO system has great feasibility and accuracy for navigation applications.

A three-point solution with scale estimation ability for two-view flat-refractive underwater photogrammetry

Underwater photogrammetry can efficiently document complex environments with limited accessibility. As the establishment of reference control networks is involved, using the relative two-view relation, which requires neither knowledge nor estimation of object-space coordinates, offers an attractive solution. In this paper, we study the two-view relation of underwater photogrammetry flat refractive geometry. Through its axial camera form, we show that the necessary correspondences to establish the relative orientation can be reduced to only three points. We also show that all six relative pose parameters can be estimated directly, where the baseline is estimated to its scale, with no external control. For the estimation, we propose direct models, and in seeking optimal minimization forms, we develop a closed-form expression for the epipolar curve. Thereby, we shed light on the epipolar geometry of the flat-refractive imaging system. For a complete evaluation, we also analyze the limits of the baseline estimation to its actual scale. In addition to our scale estimation ability, results show improved accuracy in the relative orientation parameters by order of magnitude or more compared to the state-of-the-art. Experiments demonstrate how our proposed model yields sub-millimeter levels of internal 3-space accuracy without introducing external knowledge.

The Lidargrammetric Model Deformation Method for Altimetric UAV-ALS Data Enhancement

The altimetric accuracy of aerial laser scanning (ALS) data is one of the most important issues of ALS data processing. In this paper, the authors present a previously unknown, yet simple and efficient method for altimetric enhancement of ALS data based on the concept of lidargrammetry. The generally known photogrammetric theory of stereo model deformations caused by relative orientation parameters errors of stereopair was applied for the continuous correction of lidar data based on ground control points. The preliminary findings suggest that the method is correct, efficient and precise, whilst the correction of the point cloud is continuous. The theory of the method and its implementation within the research software are presented in the text. Several tests were performed on synthetic and real data. The most significant results are presented and discussed in the article together with a discussion of the potential of lidargrammetry, and the main directions of future research are also mapped out. These results confirm that the research gap in the area of altimetric enhancement of ALS data without additional trajectory data is resolved in this study.

3D INDOOR CORRIDOR MAPPING WITH CALIBRATED ROPS OF A MULTI-FISHEYE CAMERA-RIG

Abstract. We present a 3D indoor mapping simulation in the location with limitations to access (i.e., pipeline drainage, sewer, box girder, etc.) by utilising a multi-fisheye camera rig. This camera rig (i.e., Pilot One) offers a 360 Field of View (FoV) coverage that represents an efficient data acquisition solution. Particular attention is focused on camera calibration consists of Interior Orientation Parameters (IOPs) and Relative Orientation Parameters (ROPs) to generate an accurate 3D reconstruction result. An appropriate Self-Calibration Bundle Adjustment (SCBA) approach, assisted with Australis coded targets, is conducted to have stable pre-calibration parameters that can be applied in a 68 meters corridor study field. We performed forward and backward data acquisition with unstitched original images and stitched images separately in a straight line to simulate data acquisition for future applications. However, due to the camera model and uncalibrated images, there is a “banana effect” in the stitched image. Twenty scale bars are used in this study. To achieve the ideal position and orientation of the scale bars, we attempt to construct a number of scenarios. The objective of this simulation is to determine the minimum number of scale bars that can be applied to a corridor where is difficult to access. We try to set up without any Control Scale Bars (CSB) with 20 Check Scale Bars (CkSBs). Even though this trial only has a 3.8 cm CkSBs error, it produced a length estimation error of 2.3 m (i.e., 3.37% of 68.1m) when compared with Terrestrial Laser Scanning.

ADAPTIVE WEIGHTING OF IMAGE OBSERVATIONS FOR SELF-CALIBRATION WITH FISHEYE IMAGES

Abstract. Fisheye cameras have been widely used in photogrammetric applications, but conventional techniques must be adapted to consider specific features of fisheye images, such as nonuniform resolution in the images. This work presents experimental results of an adaptive weighting of the observation in a self-calibrating bundle adjustment to cope with the nonuniform resolution of fisheye images. GoPro Fusion and Ricoh Theta dual-fisheye systems were calibrated with bundle adjustment based on equisolid-angle projection model combined with Conrady-Brown distortion model. The image observations were weighted as a function of radial distance based on combining loss of resolution and blurring in fisheye images. The results were compared with a similar trial by considering the same standard deviation for all image observations. The use of adaptive weighting of image observations reduced the estimated standard deviation of unit weight by 30 % and 50 % with GoPro Fusion and Ricoh Theta images, respectively. The estimation of relative orientation parameters (ROPs) was also improved (∼50 %) when using adaptive weighting for image observations.

Modeling Hyperhemispherical Points and Calibrating a Dual-Fish-Eye System for Close-Range Applications

Omnidirectional systems composed of two hyperhemispherical lenses (dual-fish-eye systems) are gaining popularity, but only a few works have studied suitable models for hyperhemispherical lenses and dual-fish-eye calibration. In addition, the effects of using points in the hyperhemispherical field of view in photogrammetric procedures have not been addressed. This article presents a comparative analysis of the fish-eye models (equidistant, equisolid-angle, stereographic, and orthogonal) for hyperhemispherical-lens and dual-fish-eye calibration techniques. The effects of adding points beyond 180° field of view in dual-fish-eye calibration using stability constraints of relative orientation parameters are also assessed. The experiments were performed with the Ricoh Theta dual-fish-eye system, which is composed of fish-eye lenses with a field of view of approximately 190° each. The equisolid-angle model presented the best results in the simultaneous calibration experiments. An accuracy of approximately one pixel in the object space units was achieved, showing the potential of the proposed approach for close-range applications.

Non-central refractive camera calibration using co-planarity constraints for a photogrammetric system with an optical sphere cover

Optical sphere covers are widely used in photogrammetric systems to protect camera systems from external conditions. The spherical refraction can cause severe non-linear distortion, resulting in imprecise and unreliable measurements. In particular the cameras with a relatively thick optical sphere cover (e.g. underground or underwater cameras). However, the conventional perspective camera model are not suitable for such spherical refraction calibration problem. This study presents a novel non-central refractive calibration technique for cameras with an optical sphere cover. Considering the general situation in which the camera optical axis could not pass through the spherical center, a universal spherical refraction geometric model was established with coplanarity constraints. The model introduces modified perspective center and enables the spherical refraction can be integrated into the equivalent collinearity equations, which make it possible to describe explicitly the correspondences between the image points and object coordinates. Moreover, a calibration technique is proposed to estimate relative orientation parameters. With no need to gain explicit knowledge of object space coordinates, the spherical refraction calibration can be performed with known refractive index as well as the radius of sphere cover. Numerical and experimental results demonstrated the proposed spherical refraction calibration model to be both effective and robust, which can achieve high levels of calibration accuracy.

VISION-BASED APPROACHES FOR QUANTIFYING CRACKS IN CONCRETE STRUCTURES

Abstract. In this paper, a combination of photogrammetric, computer-vision, and deep-learning approaches are proposed for accurate detection and quantification of cracks from the images of concrete structures. In particular, a semantic segmentation approach using UNet is applied, which is trained on a customized dataset of real-world images. Then, two photogrammetric methods are assessed for reconstructing the full figure of the cracks from stereo images. One approach is based on detecting the dominant structural plane surrounding the crack and projecting the crack pixels to this 3D plane. The second approach is based on matching the crack pixels across two images. To be able to perform the 3D reconstructions accurately, a rigorous calibration of the intrinsic calibration parameters of the cameras is performed. The relative orientation parameters between the stereo cameras are also determined in the calibration procedure. Extensive experiments are performed to evaluate each phase of this detection-and-quantification workflow. In general, cracks can be detected with an average precision of 87.48% and recall of 87.45%. They can be reconstructed in 3D with an accuracy as high as 0.05 mm.

NETWORK ADJUSTMENT OF AUTOMATED RELATIVE ORIENTATION FOR A DUAL-CAMERA SYSTEM

Abstract. Visual odometry (VO) is a technique applied to track the dynamic positioning and orientation of a moving platform with one or more cameras taking image sequences. The determination relies on the estimation of relative orientation parameters (ROPs) of time adjacent images. The idea of stereo VO to develop a dual-camera system is adopted in this study. By taking advantage of the calibrated stereo camera, this system is able to recover the true scale of relative translation without the need from additional sensors. However, the scale might not be very accurate, and the error also could exist in the orientation including rotation and translation due to environmental factors such as the illumination and texture. Therefore, the primary objective of this study is to find the optimized theory and method of stereo VO. Through the analysis of the geometric relationship of the time adjacent stereo image pairs, locally optimized network adjustment is developed to improve the accuracy of ROPs.The proposed network adjustment model is verified by the simulation data and experiment data both. ROPs are adopted as observations that would update the states of the image sequence further. Besides, exterior orientation parameters (EOPs) of the dual-camera system could be optimized obviously during the whole operation. In this study, it is worth mentioning that 3D coordinates of object points matched in each image pair are not necessary to be calculated. The conventional bundle adjustment is not adopted, but more accurate EOPs still have been generated automatically during the process.

DIRECT ESTIMATION OF THE RELATIVE ORIENTATION IN UNDERWATER ENVIRONMENT

Abstract. While accuracy, detail, and limited time on site make photogrammetry a valuable means for underwater mapping, the establishment of reference control networks in such settings is oftentimes difficult. In that respect, the use of the coplanarity constraint becomes a valuable solution as it requires neither knowledge of object space coordinates nor setting a reference control network. Nonetheless, imaging in such domains is subjected to non-linear and depth-dependent distortions, which are caused by refractive media that alter the standard single viewpoint geometry. Accordingly, the coplanarity relation, as formulated for the in-air case does not hold in such environment and methods that have been proposed thus far for geometrical modeling of its effect require knowledge of object-space quantities. In this paper we propose a geometrically-driven approach which fulfills the coplanarity condition and thereby requires no knowledge of object space data. We also study a linear model for the establishment of this constraints. Clearly, a linear form requires neither first approximations nor iterative convergence scheme. Such an approach may prove useful not only for object space reconstruction but also as a preparatory step for application of bundle block adjustment and for outlier detection. All are key features in photogrammetric practices. Results show that no unique setup is needed for estimating the relative orientation parameters using the model and that high levels of accuracy can be achieved.

Impact of Stereo Camera Calibration to Object Accuracy in Multimedia Photogrammetry

Camera calibration via bundle adjustment is a well-established standard procedure in single-medium photogrammetry. When using standard software and applying the collinearity equations in multimedia photogrammetry, the effects of refractive interfaces are compensated in an implicit form, hence by the usual parameters of interior orientation. This contribution analyses different calibration strategies for planar bundle-invariant interfaces. To evaluate the effects of implicitly modelling the refractive effects within bundle adjustment, synthetic error-free datasets are simulated. The behaviour of interior, exterior, and relative orientation parameters is analysed using synthetic datasets free of underwater imaging effects. A shift of the camera positions of 0.2% of the acquisition distance along the optical axis can be observed. The relative orientation of a stereo camera shows systematic effects when the angle of convergence varies. The stereo baseline increases by 1% at 25° convergence. Furthermore, the interface is set up at different distances to the camera. When the interface is at 50% distance assuming a parallel camera setup, the stereo baseline also increases by 1%. It becomes clear that in most cases the implicit modelling is not suitable for multimedia photogrammetry due to geometrical errors (scaling) and absolute positioning errors. Explicit modelling of the refractive interfaces is implemented into a bundle adjustment and is also used to analyse calibration parameters and deviations in object space. Real experiments show that it is difficult to separate the effects of implicit modelling, since other effects, such as poor image measurements, affect the final result. However, trends can be seen, and deviations are quantified.

GNSS/INS-Assisted Structure from Motion Strategies for UAV-Based Imagery over Mechanized Agricultural Fields

Acquired imagery by unmanned aerial vehicles (UAVs) has been widely used for three-dimensional (3D) reconstruction/modeling in various digital agriculture applications, such as phenotyping, crop monitoring, and yield prediction. 3D reconstruction from well-textured UAV-based images has matured and the user community has access to several commercial and opensource tools that provide accurate products at a high level of automation. However, in some applications, such as digital agriculture, due to repetitive image patterns, these approaches are not always able to produce reliable/complete products. The main limitation of these techniques is their inability to establish a sufficient number of correctly matched features among overlapping images, causing incomplete and/or inaccurate 3D reconstruction. This paper provides two structure from motion (SfM) strategies, which use trajectory information provided by an onboard survey-grade global navigation satellite system/inertial navigation system (GNSS/INS) and system calibration parameters. The main difference between the proposed strategies is that the first one—denoted as partially GNSS/INS-assisted SfM—implements the four stages of an automated triangulation procedure, namely, imaging matching, relative orientation parameters (ROPs) estimation, exterior orientation parameters (EOPs) recovery, and bundle adjustment (BA). The second strategy— denoted as fully GNSS/INS-assisted SfM—removes the EOPs estimation step while introducing a random sample consensus (RANSAC)-based strategy for removing matching outliers before the BA stage. Both strategies modify the image matching by restricting the search space for conjugate points. They also implement a linear procedure for ROPs’ refinement. Finally, they use the GNSS/INS information in modified collinearity equations for a simpler BA procedure that could be used for refining system calibration parameters. Eight datasets over six agricultural fields are used to evaluate the performance of the developed strategies. In comparison with a traditional SfM framework and Pix4D Mapper Pro, the proposed strategies are able to generate denser and more accurate 3D point clouds as well as orthophotos without any gaps.

SEAMLESS CO-REGISTRATION OF IMAGES FROM MULTI-SENSOR MULTISPECTRAL CAMERAS

Abstract. Small-format, consumer-grade multi-camera multispectral systems have gained popularity in recent years. This is specifically due to the simplicity of their integration onboard platforms with limited payload capacity, such as Unmanned Aerial Vehicles (UAVs). Commercially available photogrammetric software can process the image data collected by these cameras to create multispectral ortho-rectified mosaics. However, misalignments of several pixels between spectral bands have been observed to be a common issue when employing these solutions, which can undermine the spectral and geometric integrity of the data. Besides, in advanced processing workflows such as object detection and classification with deep learning algorithms, band-to-band co-registered images are needed rather than one mosaic. We propose a two-fold solution for seamless band-to-band registration of images captured by five cameras integrated into a miniature multispectral camera system. This approach consists of 1) a robust self-calibration of the multispectral camera system to accurately estimate the intrinsic calibration parameters and relative orientation parameters of all cameras; 2) a single capture, band-to-band co-registration method based on trifocal constraints. This approach differs from existing literature since it is fully automatic, does not make any assumptions about the scene, does not use any best-fit projective or similarity transformations, and does not attempt cross-spectral feature-point matching. Our experiments confirm that the proposed co-registration method can accurately fuse multispectral images from a miniature multi-camera system and is invariant to large depth-variations in the captured scene.

Accurate Calibration Scheme for a Multi-Camera Mobile Mapping System

Mobile mapping systems (MMS) are increasingly used for many photogrammetric and computer vision applications, especially encouraged by the fast and accurate geospatial data generation. The accuracy of point position in an MMS is mainly dependent on the quality of calibration, accuracy of sensor synchronization, accuracy of georeferencing and stability of geometric configuration of space intersections. In this study, we focus on multi-camera calibration (interior and relative orientation parameter estimation) and MMS calibration (mounting parameter estimation). The objective of this study was to develop a practical scheme for rigorous and accurate system calibration of a photogrammetric mapping station equipped with a multi-projective camera (MPC) and a global navigation satellite system (GNSS) and inertial measurement unit (IMU) for direct georeferencing. The proposed technique is comprised of two steps. Firstly, interior orientation parameters of each individual camera in an MPC and the relative orientation parameters of each cameras of the MPC with respect to the first camera are estimated. In the second step the offset and misalignment between MPC and GNSS/IMU are estimated. The global accuracy of the proposed method was assessed using independent check points. A correspondence map for a panorama is introduced that provides metric information. Our results highlight that the proposed calibration scheme reaches centimeter-level global accuracy for 3D point positioning. This level of global accuracy demonstrates the feasibility of the proposed technique and has the potential to fit accurate mapping purposes.

ON SCALE DEFINITION WITHIN CALIBRATION OF MULTI-CAMERA SYSTEMS IN MULTIMEDIA PHOTOGRAMMETRY

Abstract. In multimedia photogrammetry, multi-camera systems often provide scale by a calibrated relative orientation. Camera calibration via bundle adjustment is a well-established standard procedure in single-medium photogrammetry. When using standard software and applying the collinearity equations in multimedia photogrammetry, the refractive interfaces are modelled in an implicit form. This contribution analyses different calibration strategies for bundle-invariant interfaces. To evaluate the effects of implicitly modelling the refractive effects within a bundle adjustment, synthetic datasets are simulated. Contrary to many publications, systematic effects of the exterior orientations can be verified with simulated data. The behaviour of interior, exterior and relative orientation parameters is analysed using error-free synthetic datasets. The relative orientation of a stereo camera shows systematic effects, when the angle of convergence varies and when the synthetic interface is set up at different distances to the camera. It becomes clear, that in most cases the implicit modelling is not suitable for multimedia photogrammetry. An explicit modelling of the refractive interfaces is implemented into a bundle adjustment. This strict model is analysed and compared with the implicit form regarding systematic effects in orientation parameters as well as errors in object space. In a real experiment, the discrepancies between the implicit form using standard software and the explicit modelling using our own implementation are quantified. It is highly advisable to model the interfaces strictly, since the implicit modelling might lead to relevant errors in object space.

Structure from motion for ordered and unordered image sets based on random k-d forests and global pose estimation

In this paper, we present a new fast and robust method for structure from motion (SfM) for data sets potentially comprising thousands of ordered or unordered images. Our work focuses on the two most time-consuming procedures: (a) image matching and (b) pose estimation. For image matching, a new method employing a random k-d forest is proposed to quickly obtain pairs of overlapping images from an unordered set. After that, image matching and the estimation of relative orientation parameters are performed only for pairs found to be very likely to overlap. For pose estimation, we use a two-stage global approach, separating the determination of rotation matrices and translation parameters; the latter are computed simultaneously using a new method. In order to cope with outliers in the relative orientations, which global approaches are particularly sensitive to, we present a new constraint based on triplet loop closure errors of rotation and translation. Finally, a robust bundle adjustment is carried out to refine the image orientation parameters.We demonstrate the potential and limitations of our pipeline using various real-world datasets including ordered image data acquired from UAV (unmanned aerial vehicle) and other platforms as well as unordered data from the internet. The experiments show that our work performs better than comparable state-of-the-art SfM systems in terms of run time, while we achieve a similar accuracy and robustness.

NETWORK ADJUSTMENT OF AUTOMATIC RELATIVE ORIENTATION FROM IMAGE SEQUENCES

Abstract. Recently, Visual Odometry (VO) using cameras for navigation is known as an alternative solution in GNSS-hostile environments. VO is a process of estimating the egomotion based on consecutive frames captured by the camera. 3D Motion including the attitude and position can be described as the exterior orientation parameters (EOPs) in photogrammetry. The advantage of VO compared with wheel odometry is that VO is not affected by wheel slip in uneven terrain or other adverse conditions. Since VO computes the camera path incrementally, the errors are accumulated as well according to the motion of each new frame-to-frame over time. That would cause the drift in the estimated trajectory compared to the real path. To solve this issue, this research proposes the network adjustment model based on relative orientation parameters (ROPs) for monocular VO. The fundamental idea originates from the traverse in the field of surveying. A traverse is a series of consecutive lines whose ends have been marked in the field and whose lengths and angles have been determined from observations. Consequently, ROPs are adopted as observations in the model that would update the states of image sequence furthermore. In this research, it is worth mentioning that the coordinates of object points are not necessary to be calculated, and more accurate ROPs are improved automatically during the process. In the future, VO with this proposed method could be integrated with GNSS/INS to a navigation system.

CALIBRATION STUDY OF A TRIMBLE ACx4 SYSTEM FOR DIRECT GEOREFERENCING MAPPING APPLICATIONS

Abstract. The Trimble Aerial Camera x4 (i.e., TACx4) is a photogrammetric multi-head system manufactured by Trimble Inc.© in 2010. It has four cameras mounted together in the main structure allowing the simultaneous acquisition to generate a single synthetic image with much larger ground coverage. In addition, the cameras are also integrated with a GNSS/INS to perform “Direct” or “Integrated” Sensor Orientation. The main condition to obtain photogrammetric mapping products with high accuracy using a direct sensor orientation procedure is to execute a step known as “geometric system calibration”. In general, the photogrammetric multi-head system manufacturers perform this step using laboratory methods to obtain the parameters of cameras interior and relative orientation. Accurate mounting parameters (lever arms and “boresight misalignments”) are fundamental requirements to generate the synthetic image when georeferencing of images is applied. This paper shows a “full field” calibration method to perform the geometric system calibration of the TACx4 system and its evaluation for direct sensor orientation mapping applications. The developed method involves two steps using only aerial images: (1) estimation of the cameras interior and relative orientation parameters to generate the synthetic image and (2) estimation of the synthetic image interior orientation and the mounting parameters between the synthetic image and GNSS/INS reference systems using two different methods. The obtained results in the conventional photogrammetric project show that the proposed method allows performing the geometric system calibration of the TACx4 system achieving around 50 cm (5 pixels) in horizontal and vertical accuracies. The obtained results can be used for large-scale mapping requirements using direct sensor orientation according to Brazilian accuracy standards.

ROBUST AND ACCURATE IMAGE-BASED GEOREFERENCING EXPLOITING RELATIVE ORIENTATION CONSTRAINTS

Abstract. Urban environments with extended areas of poor GNSS coverage as well as indoor spaces that often rely on real-time SLAM algorithms for camera pose estimation require sophisticated georeferencing in order to fulfill our high requirements of a few centimeters for absolute 3D point measurement accuracies. Since we focus on image-based mobile mapping, we extended the structure-from-motion pipeline COLMAP with georeferencing capabilities by integrating exterior orientation parameters from direct sensor orientation or SLAM as well as ground control points into bundle adjustment. Furthermore, we exploit constraints for relative orientation parameters among all cameras in bundle adjustment, which leads to a significant robustness and accuracy increase especially by incorporating highly redundant multi-view image sequences. We evaluated our integrated georeferencing approach on two data sets, one captured outdoors by a vehicle-based multi-stereo mobile mapping system and the other captured indoors by a portable panoramic mobile mapping system. We obtained mean RMSE values for check point residuals between image-based georeferencing and tachymetry of 2 cm in an indoor area, and 3 cm in an urban environment where the measurement distances are a multiple compared to indoors. Moreover, in comparison to a solely image-based procedure, our integrated georeferencing approach showed a consistent accuracy increase by a factor of 2–3 at our outdoor test site. Due to pre-calibrated relative orientation parameters, images of all camera heads were oriented correctly in our challenging indoor environment. By performing self-calibration of relative orientation parameters among respective cameras of our vehicle-based mobile mapping system, remaining inaccuracies from suboptimal test field calibration were successfully compensated.