Abstract
Abstract. Mobile Mapping (MM) has gained significant importance in the realm of high-resolution data acquisition techniques. MM is able to record georeferenced street-level data in a continuous (laser scanners) and/or discrete (cameras) fashion. MM’s georeferencing relies on a conjunction of Global Navigation Satellite Systems (GNSS), Inertial Measurement Units (IMU) and optionally on odometry sensors. While this technique does not pose a problem for absolute positioning in open areas, its reliability and accuracy may be diminished in urban areas where high-rise buildings and other tall objects can obstruct the direct line-of-sight between the satellite and the receiver unit. Consequently, multipath measurements or complete signal outages impede the MM platform’s localisation and may affect the accurate georeferencing of collected data. This paper presents a technique to recover correct orientation parameters for MM imaging platforms by utilising aerial images as an external georeferencing source. This is achieved by a fully automatic registration strategy which takes into account the overall differences between aerial and MM data, such as scale, illumination, perspective and content. Based on these correspondences, MM data can be verified and/or corrected by using an adjustment solution. The registration strategy is discussed and results in a success rate of about 95 %.
Highlights
As a mobile, but terrestrial acquisition method, Mobile Mapping has become an important supplement to traditional geo-data acquisition techniques
Mobile Mapping is primarily useful in urban areas
An adjustment solution has to be designed to account for this case
Summary
But terrestrial acquisition method, Mobile Mapping has become an important supplement to traditional geo-data acquisition techniques. The direct line-of-sight between the GNSS receiver and the satellite may be obstructed and the signal from the satellite might not reach the receiver, or the signal is reflected at façades or other objects and is received delayed in time. Both scenarios have a different impact on the position estimation, the resulting accuracy and reliability are potentially decreased. The main contribution is the introduction of a two-step registration mechanism based on an approximated transformation between the MM and the aerial data set to enable a reliable feature matching procedure robust against repeated patterns, illumination changes, and other differing image properties, such as original perspective or to some extent image content. The adjustment procedure of MM images will be presented
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