Proceedings International Workshop on Mobile Mapping Technology

li, rongxing

Bibliographic data

Monograph

Persistent identifier:: 856671290

Author:: Li, Rongxing

Title:: Proceedings International Workshop on Mobile Mapping Technology

Sub title:: April 21 - 23, 1999, Bangkok, Thailand

Scope:: 1 Online-Ressource (Getr. Zählung [ca. 400 Seiten])

Year of publication:: 1999

Place of publication:: London

Publisher of the original:: RICS Books

Identifier (digital):: 856671290

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: [Session 4: Sensor Integration and Calibration]

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: SENSOR INTEGRATION AND CALIBRATION OF DIGITAL AIRBORNE THREE-LINE CAMERA SYSTEMS. Michael Cramer, Dirk Stallmann and Norbert Haala.

Document type:: Monograph

Structure type:: Chapter

for the photo flights. The rotational offsets between the INS sen sor axes and the camera coordinate system cannot be observed via conventional survey methods. Therefore, these rotational off set or misalignment angles between the INS and camera system have to be determined with in-flight calibration using a small num ber of tie and control points. Nevertheless, if there are no relative movements between the different sensor components, these off sets should remain constant for several survey campaigns. There is some ongoing work to prove the stability of these displacements over a longer period of time. The quality of the integrated GPS/INS positions and attitudes is highly correlated with the quality of the updating information from GPS. Even though the INS informations can be used to bridge short GPS outages or to detect small cycle slips of the carrier phase measurements, the overall performance will degrade if the GPS position and velocity update informations are of minor quality for a longer time interval. The inertial data can only be used to detect GPS short term failures. The correction of long term sys tematic errors is not possible. Especially in case of photogram- metric applications where the distance between remote and mas ter receiver can be very large due to operational reasons, at least constant offsets for GPS positions have to be expected resulting from insufficient modeling of the atmospheric errors. Additionally, errors might be introduced from incorrect datum parameters for datum shift, remaining systematic effects from the imaging sensor or - quite simple - erroneous reference coordinates of the mas ter station. Within the standard approach of GPS supported aerial triangulation these remaining systematic errors are introduced as additional unknowns and compensated in the bundle block adjust ment. Such an approach is not possible for the “simple” GPS/INS integration using a Kalman filter, as far as no informations from image space are used. In other words, every error that is not mod eled in the dynamic model of the filter will introduce errors in the georeferencing process. 3 COMBINING GPS AND INS WITH AERIAL TRIANGULATION Similar to GPS supported aerial triangulation an integrated ap proach should be applied for the georeferencing of imagery by combining and utilizing as many informations from different sen sors as possible, i.e. GPS, INS, and informations from image space. This approach should • • enable a control of the georeferencing process by increasing the reliability of the whole system. • allow an operational processing in terms of - the number of required tie and control points, which should be less or equal compared to standard aerial triangulation with full frame imagery. - the potential of an automated processing. • enable a self-calibration of the camera. • provide a higher accuracy compared to direct georeferencing by GPS/INS integration, particularly if only data for the single image strips are available. 3.1 GPS/INS data processing In contrary to the GPS/INS processing proposed i.e. by (Schwarz, 1995), (Skaloud, 1995), (Sherzinger, 1997) within the algorithm presented here, no Kalman filter is used. Originally, this algorithm was designed for processing of the data from the DPA sensor sys tem - a three line push-broom scanner, that will be described in more detail in section 5 -, where inertial data are available only during the acquisition of image strips due to hardware restrictions. The lack of a continuous INS data trajectory prevents the standard Kalman approach starting with a static initial alignment for position and attitude. Therefore, the initial alignment has to be done in flight, during the motion of the aircraft. Usually, this in-flight align ment is obtained from gyrocompassing (mainly for roll and pitch) and the combination of GPS derived velocities to the inertial mea surements during aircraft maneuvers, which are performed to pro voke accelerations in all directions (mainly for heading). As there are almost no accelerations during the image strips, this method is not applicable to determine the initial attitudes, in especially the heading angle. Therefore the basic concept of the algorithm, which is presented in figure 1 is as follows. First a strap-down INS mechanization is per formed, which is supported by the GPS measurements. If there is no additional information available the initial offsets (accelerometer bias, gyro bias) of the inertial sensor are assumed to be zero for the first mechanization step of the INS data. The initial position and velocity are obtained from GPS. Assuming a normal flight, the ini tial orientation of the system will be close to zero for the roll u> and pitch angle ip. The initial heading k is obtained from GPS. Using the estimated initial alignment and the sensor offsets, the mech anization is done, whereas the INS derived positions are updated via GPS at every GPS measurement epoch. After integration the parameters of exterior orientation (position Xi,Yi,Zi, attitude u>i, ipi, «¿) are available for every measurement epoch i. The positioning accuracy is mainly dependent on the ac curacy of the GPS .positioning. The attitudes are mainly corrupted by a constant offset ojo,¥>o,«o due to the incorrect initial align ment. Additionally, there are some drift errors wi,(pi,Ki caused by remaining sensor offsets. These errors have to be determined and corrected (equation 1) to obtain corrected attitudes u>i,<pi,Ri and to get highest accuracies for the georeferencing. U>i + UJO + U)\ -t <Pi + <po + <Pi • t Ki + KQ + Kl ■ t (1) Equation 1 is a simplification of the true error behaviour. Additional errors introduced due to the correlations between the attitudes are not considered here. The effects caused by correlations are de scribed in section 4 in more detail. Nevertheless, applying this er ror model in an iterative process of a combined aerial triangulation, the best solution will be obtained after a few iteration steps. In addition to the INS error terms, the orientations are affected by the unknown misalignment Su, 5<p, 5k between the INS body b and the image coordinate frame p. 3.2 Combined aerial triangulation The general idea is to perform an aerotriangulation of imagery in order to correct the position and attitude, which are provided from the GPS/INS module. Similar to the approach proposed by (Gib son, 1994), these terms contain INS error terms, as well as pa rameters for system calibration resulting from the physical offsets of the different sensors. Although the algorithmn was developed for the evaluation of line scanner imagery, the data of traditional frame sensors combined with a GPS/inertial module can be processed in the same way. Similar to the Kalman filter concept, the errors are grouped in an error state vector. This vector includes the navigation errors, the sensor noise terms and can be expanded by additional calibration terms. After mechanization the error terms are updated using the values estimated in the aerotriangulation step. Within this aerial triangulation the photogrammetric coplanarity (relative orientation) and collinearity (absolute orientation) are used for the estimation of the error terms. For reasons of simplification and flexibility the collinearity equation will be utilized in the following. 4-5-3 M I BU JB _ .„.jisjMMHMIMi 1S: Sft

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