Exterior Orientation Parameters Of Images Research Articles

A Study on In Situ Calibration of an Off-The-Shelf Digital Camera Integrated to a Lidar System

Photogrammetric procedures using the integration of imagery and light detection and ranging (Lidar) datasets are becoming common in mapping companies nowadays. When photogrammetric and Lidar surveys are performed simultaneously while having the camera connected to the Lidar system, the image's exterior orientation parameters computed from the Lidar system can be used to perform photogrammetric applications. To implement such approach, camera calibration procedures should be applied to obtain accurate interior orientation parameters. This paper shows the performed study and experimental results from an in situ self-calibration using simultaneously collected imagery and Lidar datasets. The experimental study is conducted to devise a methodology for the in situ self-calibration that uses a set of vertical control points, extracted from Lidar point cloud, as well as the three-dimensional coordinates of the camera exposure stations, computed from the Lidar trajectory. For the small photogrammetric block used in this study, the results from the performed experiments demonstrated that the in situ self-calibration is required to obtain horizontal accuracies of approximately one image pixel on the ground in bundle adjustment assisted by direct measured of the coordinates of the camera exposure stations.

MATCHING AERIAL IMAGES TO 3D BUILDING MODELS BASED ON CONTEXT-BASED GEOMETRIC HASHING

Abstract. In this paper, a new model-to-image framework to automatically align a single airborne image with existing 3D building models using geometric hashing is proposed. As a prerequisite process for various applications such as data fusion, object tracking, change detection and texture mapping, the proposed registration method is used for determining accurate exterior orientation parameters (EOPs) of a single image. This model-to-image matching process consists of three steps: 1) feature extraction, 2) similarity measure and matching, and 3) adjustment of EOPs of a single image. For feature extraction, we proposed two types of matching cues, edged corner points representing the saliency of building corner points with associated edges and contextual relations among the edged corner points within an individual roof. These matching features are extracted from both 3D building and a single airborne image. A set of matched corners are found with given proximity measure through geometric hashing and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on co-linearity equations. The result shows that acceptable accuracy of single image's EOP can be achievable by the proposed registration approach as an alternative to labour-intensive manual registration process.

MATCHING AERIAL IMAGES TO 3D BUILDING MODELS BASED ON CONTEXT-BASED GEOMETRIC HASHING

In this paper, a new model-to-image framework to automatically align a single airborne image with existing 3D building models using geometric hashing is proposed. As a prerequisite process for various applications such as data fusion, object tracking, change detection and texture mapping, the proposed registration method is used for determining accurate exterior orientation parameters (EOPs) of a single image. This model-to-image matching process consists of three steps: 1) feature extraction, 2) similarity measure and matching, and 3) adjustment of EOPs of a single image. For feature extraction, we proposed two types of matching cues, edged corner points representing the saliency of building corner points with associated edges and contextual relations among the edged corner points within an individual roof. These matching features are extracted from both 3D building and a single airborne image. A set of matched corners are found with given proximity measure through geometric hashing and optimal matches are then finally determined by maximizing the matching cost encoding contextual similarity between matching candidates. Final matched corners are used for adjusting EOPs of the single airborne image by the least square method based on co-linearity equations. The result shows that acceptable accuracy of single image's EOP can be achievable by the proposed registration approach as an alternative to labour-intensive manual registration process.

Photogrammetric mapping using unmanned aerial vehicle

Abstract. Nowadays Unmanned Aerial Vehicle (UAV) technology has attracted attention for aerial photogrammetric mapping. The low cost and the feasibility to automatic flight along commanded waypoints can be considered as the main advantages of this technology in photogrammetric applications. Using GNSS/INS technologies the images are taken at the planned position of the exposure station and the exterior orientation parameters (position Xo, Yo, Zo and attitude ω, φ, χ) of images can be direct determined. However, common UAVs (off-the-shelf) do not replace the traditional aircraft platform. Overall, the main shortcomings are related to: difficulties to obtain the authorization to perform the flight in urban and rural areas, platform stability, safety flight, stability of the image block configuration, high number of the images and inaccuracies of the direct determination of the exterior orientation parameters of the images. In this paper are shown the obtained results from the project photogrammetric mapping using aerial images from the SIMEPAR UAV system. The PIPER J3 UAV Hydro aircraft was used. It has a micro pilot MP2128g. The system is fully integrated with 3-axis gyros/accelerometers, GPS, pressure altimeter, pressure airspeed sensors. A Sony Cyber-shot DSC-W300 was calibrated and used to get the image block. The flight height was close to 400 m, resulting GSD near to 0.10 m. The state of the art of the used technology, methodologies and the obtained results are shown and discussed. Finally advantages/shortcomings found in the study and main conclusions are presented

Camera Pose Determination and 3-D Measurement From Monocular Oblique Images With Horizontal Right Angle Constraints

This letter introduces a novel method for camera pose determination from monocular urban oblique images. Horizontal right angles that widely exist in urban scenes are used as geometric constraints in the camera pose determination, and the proposed 3-D measurement method using a monocular image is presented and then used to check the accuracy of the recovered image's exterior orientation parameters. Compared to the available vertical-line-based camera pose determination method, our new method is more accurate.

Precise calibration of a rotation photogrammetric system

Rotation photogrammetric systems are widely used for 3D information acquisition, where high-precision calibration is one of the critical steps. This study first shows how to derive the rotation model and deviation model in the object space coordinate system according to the basic structure of the system and the geometric relationship of the related coordinate systems. Then, overall adjustment of multi-images from a surveying station is employed to calibrate the rotation matrix and the deviation matrix of the system. The exterior orientation parameters of images captured by other surveying stations can be automatically calculated for 3D reconstruction. Finally, real measured data from Wumen wall of the Forbidden City is employed to verify the performance of the proposed calibration method. Experimental results show that this method is accurate and reliable and that a millimetre level precision can be obtained in practice.

Exterior orientation of CBERS-2B imagery using multi-feature control and orbital data

The major contribution of this paper relates to the practical advantages of combining Ground Control Points (GCPs), Ground Control Lines (GCLs) and orbital data to estimate the exterior orientation parameters of images collected by CBERS-2B (China–Brazil Earth Resources Satellite) HRC (High-resolution Camera) and CCD (High-resolution CCD Camera) sensors. Although the CBERS-2B is no longer operational, its images are still being used in Brazil, and the next generations of the CBERS satellite will have sensors with similar technical features, which motivates the study presented in this paper. The mathematical models that relate the object and image spaces are based on collinearity (for points) and coplanarity (for lines) conditions. These models were created in an in-house developed software package called TMS (Triangulation with Multiple Sensors) with multi-feature control (GCPs and GCLs). Experiments on a block of four CBERS-2B HRC images and on one CBERS-2B CCD image were performed using both models. It was observed that the combination of GCPs and GCLs provided better bundle block adjustment results than conventional bundle adjustment using only GCPs. The results also demonstrate the advantages of using primarily orbital data when the number of control entities is reduced.

AEROTRIANGULATION SUPPORTED BY CAMERA STATION POSITION DETERMINED VIA PHYSICAL INTEGRATION OF LIDAR AND SLR DIGITAL CAMERA

Abstract. Nowadays lidar and photogrammetric surveys have been used together in many mapping procedures due to their complementary characteristics. Lidar survey is capable to promptly acquire reliable elevation information that is sometimes difficult via photogrammetric procedure. On the other hand, photogrammetric survey is easily able to get semantic information of the objects. Accessibility, availability, the increasing sensor size and quick image acquisition and processing are properties that have raised the use of SLR digital cameras in photogrammetry. Orthoimage generation is a powerful photogrammetric mapping procedure, where the advantages of the integration of lidar and image datasets are very well characterized. However, to perform this application both datasets must be within a common reference frame. In this paper, a procedure to have digital images positioned and oriented in the same lidar frame via a combination of direct and indirect georeferencing is studied. The SLR digital camera was physically connected with the lidar system to calculate the camera station's position in lidar frame. After that, the aerotriangulation supported by camera station's position is performed to get image's exterior orientation parameters (EOP).

DETERMINATION OF EXTERIOR ORIENTATION PARAMETERS THROUGH DIRECT GEO-REFERENCING IN A REAL-TIME AERIAL MONITORING SYSTEM

Abstract. Rapid responses for emergency situations such as natural disasters or accidents often require geo-spatial information describing the on-going status of the affected area. Such geo-spatial information can be promptly acquired by a manned or unmanned aerial vehicle based multi-sensor system that can monitor the emergent situations in near real-time from the air using several kinds of sensors. Thus, we are in progress of developing such a real-time aerial monitoring system (RAMS) consisting of both aerial and ground segments. The aerial segment acquires the sensory data about the target areas by a low-altitude helicopter system equipped with sensors such as a digital camera and a GPS/IMU system and transmits them to the ground segment through a RF link in real-time. The ground segment, which is a deployable ground station installed on a truck, receives the sensory data and rapidly processes them to generate ortho-images, DEMs, etc. In order to generate geo-spatial information, in this system, exterior orientation parameters (EOP) of the acquired images are obtained through direct geo-referencing because it is difficult to acquire coordinates of ground points in disaster area. The main process, since the data acquisition stage until the measurement of EOP, is discussed as follows. First, at the time of data acquisition, image acquisition time synchronized by GPS time is recorded as part of image file name. Second, the acquired data are then transmitted to the ground segment in real-time. Third, by processing software for ground segment, positions/attitudes of acquired images are calculated through a linear interpolation using the GPS time of the received position/attitude data and images. Finally, the EOPs of images are obtained from position/attitude data by deriving the relationships between a camera coordinate system and a GPS/IMU coordinate system. In this study, we evaluated the accuracy of the EOP decided by direct geo-referencing in our system. To perform this, we used the precisely calculated EOP through the digital photogrammetry workstation (DPW) as reference data. The results of the evaluation indicate that the accuracy of the EOP acquired by our system is reasonable in comparison with the performance of GPS/IMU system. Also our system can acquire precise multi-sensory data to generate the geo-spatial information in emergency situations. In the near future, we plan to complete the development of the rapid generation system of the ground segment. Our system is expected to be able to acquire the ortho-image and DEM on the damaged area in near real-time. Its performance along with the accuracy of the generated geo-spatial information will also be evaluated and reported in the future work.

IMAGE TO POINT CLOUD METHOD OF 3D-MODELING

Abstract. This article describes the method of constructing 3D models of objects (buildings, monuments) based on digital images and a point cloud obtained by terrestrial laser scanner. The first step is the automated determination of exterior orientation parameters of digital image. We have to find the corresponding points of the image and point cloud to provide this operation. Before the corresponding points searching quasi image of point cloud is generated. After that SIFT algorithm is applied to quasi image and real image. SIFT algorithm allows to find corresponding points. Exterior orientation parameters of image are calculated from corresponding points. The second step is construction of the vector object model. Vectorization is performed by operator of PC in an interactive mode using single image. Spatial coordinates of the model are calculated automatically by cloud points. In addition, there is automatic edge detection with interactive editing available. Edge detection is performed on point cloud and on image with subsequent identification of correct edges. Experimental studies of the method have demonstrated its efficiency in case of building facade modeling.