Acquisition and Rectification of Shape Data Obtained by a Moving Range Sensor

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“Modeling from Reality” techniques are making great progress because of the availability of accurate geometric data from three-dimensional digitizers. These techniques contribute to numerous applications in many areas. Among them one of the most important and comprehensive　applications is modeling cultural heritage objects. For a large object scanning　from the air is one of the most efficient methods for obtaining 3D data. We developed a　novel 3D measurement system the Floating Laser Range Sensor (FLRS) in which a range　sensor is suspended beneath a balloon. The obtained data however have some distortions　due to movement of the system during the scanning process. We propose two novel methods　to rectify the shape data obtained by the moving range sensor. One method rectifies the data　by using image sequences; the other rectifies the data without images. To test these methods we have conducted a digital archiving project of a large-scale heritage object in which our　algorithms are applied. The results show the effectiveness of our methods. Moreover both　methods are applicable not only to our FLRS but also to moving range sensors in general.

“Modeling from Reality” techniques are making great progress because of the availability of accurate geometric data from three-dimensional digitizers. These techniques contribute to numerous applications in many areas. Among them, one of the most important and comprehensive　applications is modeling cultural heritage objects. For a large object, scanning　from the air is one of the most efficient methods for obtaining 3D data. We developed a　novel 3D measurement system, the Floating Laser Range Sensor (FLRS), in which a range　sensor is suspended beneath a balloon. The obtained data, however, have some distortions　due to movement of the system during the scanning process. We propose two novel methods　to rectify the shape data obtained by the moving range sensor. One method rectifies the data　by using image sequences; the other rectifies the data without images. To test these methods, we have conducted a digital archiving project of a large-scale heritage object, in which our　algorithms are applied. The results show the effectiveness of our methods. Moreover, both　methods are applicable not only to our FLRS, but also to moving range sensors in general.

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