Abstract
This paper tackles the first step of any strategy aiming to improve the trajectory of terrestrial mobile mapping systems in urban environments. We present an approach to model the error of terrestrial mobile mapping trajectories, combining deterministic and stochastic models. Due to urban specific environment, the deterministic component will be modelled with non-continuous functions composed by linear shifts, drifts or polynomial functions. In addition, we will introduce a stochastic error component for modelling residual noise of the trajectory error function. <br><br> First step for error modelling requires to know the actual trajectory error values for several representative environments. In order to determine as accurately as possible the trajectories error, (almost) error less trajectories should be estimated using extracted nonsemantic features from a sequence of images collected with the terrestrial mobile mapping system and from a full set of ground control points. Once the references are estimated, they will be used to determine the actual errors in terrestrial mobile mapping trajectory. The rigorous analysis of these data sets will allow us to characterize the errors of a terrestrial mobile mapping system for a wide range of environments. This information will be of great use in future campaigns to improve the results of the 3D points cloud generation. <br><br> The proposed approach has been evaluated using real data. The data originate from a mobile mapping campaign over an urban and controlled area of Dortmund (Germany), with harmful GNSS conditions. The mobile mapping system, that includes two laser scanner and two cameras, was mounted on a van and it was driven over a controlled area around three hours. The results show the suitability to decompose trajectory error with non-continuous deterministic and stochastic components.
Highlights
Technology progress, society needs and a limited availability of funds, have changed the way 3D data are collected, from the acquisition sensors point of view and from the platforms point of view
The final goal of this research is to provide knowledge to improve a trajectory of Terrestrial mobile mapping (TMM) system by rigorous modelling its error in two weak scenarios: when the available ground control points are limited and in scenarios with a weak geometry or non-continuous capability to extract and match non-semantic features
We present a strategy to determine as accurately as possible the trajectory error of a TMM system in urban scenarios
Summary
Technology progress, society needs and a limited availability of funds, have changed the way 3D data are collected, from the acquisition sensors point of view and from the platforms point of view Examples of this manned or unmanned platforms are satellites, planes, cars, bikes or more recently rovers, trolleys or even a mobile phones. TMM has gained popularity allowing easy access to geoinformation, with low accuracy, thanks to Google street view family systems and it might be boosted with experiences such as Google tango project for indoor mapping. Nowadays many applications such as 3D city modelling, cadastral mapping, cultural heritage, facility management, autonomous driving take benefit of 3D georeferenced data, or point cloud (Kutterer, 2010). The applications mentioned above can be grouped into three levels according to their point cloud accuracy requirements: high (
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