Abstract
Asset Management is an important component of an infrastructure project. A significant cost is involved in maintaining and updating the asset information. Data collection is the most time-consuming task in the development of an asset management system. In order to reduce the time and cost involved in data collection, this paper proposes a low cost Mobile Mapping System using an equipped laser scanner and cameras. First, the feasibility of low cost sensors for 3D asset inventory is discussed by deriving appropriate sensor models. Then, through calibration procedures, respective alignments of the laser scanner, cameras, Inertial Measurement Unit and GPS (Global Positioning System) antenna are determined. The efficiency of this Mobile Mapping System is experimented by mounting it on a truck and golf cart. By using derived sensor models, geo-referenced images and 3D point clouds are derived. After validating the quality of the derived data, the paper provides a framework to extract road assets both automatically and manually using techniques implementing RANSAC plane fitting and edge extraction algorithms. Then the scope of such extraction techniques along with a sample GIS (Geographic Information System) database structure for unified 3D asset inventory are discussed.
Highlights
The lifetime of Civil Engineering infrastructure projects are generally made up of three phases—design and planning, construction and maintenance
Considering the importance of a detailed asset inventory database and the limitations of traditional surveying methods to achieve that, there is a need for an economic solution with improved capabilities
A cost-effective mobile mapping system was built to provide a solution to the limitations posed by the previous techniques
Summary
The lifetime of Civil Engineering infrastructure projects are generally made up of three phases—design and planning, construction and maintenance. In order to make the process of maintenance and improvements more manageable, it is necessary to document the assets and inventories in an appropriate format. Spatial data about roadside asset inventories and other municipal utilities are generally collected using traditional land survey methods. The field survey methods use GPS and total stations to record the location of various assets. Though manual survey methods are highly accurate in two-dimensional space, it is difficult to obtain the third dimension of points with high accuracy. These methods are time consuming and cumbersome, as well [3]. There are several other data management and data integrity challenges associated with using traditional survey methods for replenishing asset inventory database. There are cases where the data is incoherently spread across different systems, in different formats, depending on the survey crew and temporal aspects of data collection
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