IMAGE ACQUISITION CONSTRAINTS FOR PANORAMIC FRAME CAMERA IMAGING

The paper describes an approach to quantify the amount of projective error produced by an offset of projection centres in a panoramic imaging workflow. We have limited this research to such panoramic workflows in which several sub-images using planar image sensor are taken and then stitched together as a large panoramic image mosaic. The aim is to simulate how large the offset can be before it introduces significant error to the dataset. The method uses geometrical analysis to calculate the error in various cases. Constraints for shooting distance, focal length and the depth of the area of interest are taken into account. Considering these constraints, it is possible to safely use even poorly calibrated panoramic camera rig with noticeable offset in projection centre locations. The aim is to create datasets suited for photogrammetric reconstruction. Similar constraints can be used also for finding recommended areas from the image planes for automatic feature matching and thus improve stitching of sub-images into full panoramic mosaics. The results are mainly designed to be used with long focal length cameras where the offset of projection centre of sub-images can seem to be significant but on the other hand the shooting distance is also long. We show that in such situations the error introduced by the offset of the projection centres results only in negligible error when stitching a metric panorama. Even if the main use of the results is with cameras of long focal length, they are feasible for all focal lengths

REGISTRATION OF LASER SCANNING POINT CLOUDS AND AERIAL IMAGES USING EITHER ARTIFICIAL OR NATURAL TIE FEATURES

Integration of laser scanning data and photographs is an excellent combination regarding both redundancy and complementary. Applications of integration vary from sensor and data calibration to advanced classification and scene understanding. In this research, only airborne laser scanning and aerial images are considered. Currently, the initial registration is solved using direct orientation sensors GPS and inertial measurements. However, the accuracy is not usually sufficient for reliable integration of data sets, and thus the initial registration needs to be improved. A registration of data from different sources requires searching and measuring of accurate tie features. Usually, points, lines or planes are preferred as tie features. Therefore, the majority of resent methods rely highly on artificial objects, such as buildings, targets or road paintings. However, in many areas no such objects are available. For example in forestry areas, it would be advantageous to be able to improve registration between laser data and images without making additional ground measurements. Therefore, there is a need to solve registration using only natural features, such as vegetation and ground surfaces. Using vegetation as tie features is challenging, because the shape and even location of vegetation can change because of wind, for example. The aim of this article was to compare registration accuracies derived by using either artificial or natural tie features. The test area included urban objects as well as trees and other vegetation. In this area, two registrations were performed, firstly, using mainly built objects and, secondly, using only vegetation and ground surface. The registrations were solved applying the interactive orientation method. As a result, using artificial tie features leaded to a successful registration in all directions of the coordinate system axes. In the case of using natural tie features, however, the detection of correct heights was difficult causing also some tilt errors. The planimetric registration was accurate

Wired and wireless camera triggering with Arduino

Synchronous triggering is an important task that allows simultaneous data capture from multiple cameras. Accurate synchronization enables 3D measurements of moving objects or from a moving platform. In this paper, we describe one wired and four wireless variations of Arduino-based low-cost remote trigger systems designed to provide a synchronous trigger signal for industrial cameras. Our wireless systems utilize 315MHz or 434MHz frequencies with noise filtering capacitors. In order to validate the synchronization accuracy, we developed a prototype of a rotating trigger detection system (named RoTriDeS). This system is suitable to detect the triggering accuracy of global shutter cameras. As a result, the wired system indicated an 8.91 µs mean triggering time difference between two cameras. Corresponding mean values for the four wireless triggering systems varied between 7.92 and 9.42 µs. Presented values include both camera-based and trigger-based desynchronization. Arduino-based triggering systems appeared tobe feasible, and they have the potential to be extended to more complicated triggering systems.Peer reviewe

Designing and building a cost-efficient survey drone

In this paper, we present a workflow how to design and implement a low-cost survey drone that meets the quality requirements of a much higher cost drone system. The technical specifications of available components and our design boundaries were applied in eCalc RC - xcopterCalc calculator in which the optimal setup was found by simulation. The main boundaries of design were derived from safety, operation time and payload capacity. Pixhawk 2 FCU, which is based on ArduPilot open source platform, was selected to handle autopilot and control functionalities. In addition, the system included a camera and a gimbal. The camera was controlled by FCU, which allows to geotag images using the on-board GPS data. The assembled survey drone was tested in a real survey mission. We successfully managed to complete a 13 minutes survey mission in mild wind conditions. According to simulation, the expected flight time range was between 9 and 15 minutes. In addition, simulation provided useful information on how the drone worksunder certain conditions such as working in extreme temperatures or high elevation locations as well as under heavy payloads. Even though our example was a survey drone, it is possible to use the same principles to design and implement a drone suitable for other tasks.Peer reviewe

Quality Analysis and Correction of Mobile Backpack Laser Scanning Data

Peer reviewe