- Research article
- Open Access
Uncertainty-aware visualization and proximity monitoring in urban excavation: a geospatial augmented reality approach
© Su et al.; licensee Springer. 2013
- Received: 1 January 2013
- Accepted: 31 January 2013
- Published: 12 June 2013
This research aims to improve the urban excavation safety by creating an uncertainty-aware, geospatial augmented reality (AR) to visualize and monitor the proximity between invisible utilities and digging implements. Excavation is the single largest cause of utility strikes. Utility strikes could be prevented if the excavator operator were able to “see” buried utilities and excavator movement, and judge the proximity between them in real time. Geospatial augmented reality (AR) is an enabling technology for such knowledge-based excavation. It synergizes the geospatial utility locations and the excavator movement into a real-time, three-dimensional (3D) spatial context accessible to excavator operators. The key to its success is the quality of the utility location data.
This paper describes a dynamic approach to incorporate positional uncertainties of buried utilities into an uncertainty-aware, geospatial-AR system for real time visualization and proximity analysis. Uncertainties are modeled as probability bands (e.g. spatial bands with certain probabilities of enclosing the “true” location of utilities). Positional uncertainties are derived in real time by referring to its determinant, data lineage, the genesis and processes used to collect and interpret data.
A computational framework, and a generic data model and its XML-format implementation are developed and tested. A method is developed to analyze the proximity in the context of positional uncertainties of both the utilities and the excavator movement.
This newly created approach is expected to contribute to the safety in urban excavation via the integration of Geoinformatics and construction informatics into an uncertainty-aware, geospatial-AR, with real time visualization and analytical capabilities.
- Buried utilities
- Error modeling
- Uncertainty modeling
- Virtual reality
Underground utilities are critical components of the massive utility networks that provide basic services to the society. It is estimated that the total length of underground utilities including water, sewer, gas, electrical, and telecom in the US is in excess of 35 million miles. The largest single threat to the safety of underground utilities is excavation (National Transportation Safety Board (NTSB) (2000) 1998; National Transportation Safety Board (NTSB) 1997). In the US, underground utilities are hit or damaged by excavation every 60 seconds (Spurgin et al. 2009; Common Ground Alliance (CGA) 2010).
Besides the high frequency of its occurrence, a hit on utilities by an excavation operation often leads to disastrous consequences in aspects of disruption to services, property damage, deaths, and serious injuries (Felt 2007;Nelson and Daly 1998;Doctor et al. 1995). For instance, the natural gas pipeline rupture and subsequent explosion caused by excavation in St. Cloud, Minnesota on December 11, 1998 caused four fatal injuries, one serious injury, and 10 minor injuries; and destroyed six buildings (National Transportation Safety Board (NTSB) (2000) 1998; National Transportation Safety Board (NTSB) 1997). In 2007, the excavation strike on a high pressure gas main in Cary, North Carolina resulted in a 100 feet high fireball that burned for nearly six hours and consequently, the evacuation of nearby residents and closing of major roads (WRAL archives 2011). The Office of Pipeline Safety’s Pipeline and Hazardous Materials Safety Administration (PHMSA) reported a total of 2770 serious incidents from year 2001 to 2010, of which nearly 20% (544 incidents) were excavation related and caused a total of 37 fatalities, 152 injuries, and $200 million in property damage (PHMSA 2011).
Despite the implementation of the 811 One-Call System that requires excavation contractors to call the state One-Call center that in turn, informs utility owners to mark utility locations with spray paint or flags, excavation remains the single largest cause of pipeline accidents. For instance, a UNCC (Utility Notification Center of Colorado (UNCC) 2005) study reported that 55.7% of the 9,371 incidents in Colorado in 2005 occurred even though the excavators followed the One-Call procedure. These incidents occur due to two primary reasons: (1) reliable data regarding the true location of underground utilities is missing or incomplete, i.e., utilities are often NOT at locations where the records specify (Sterling et al. 2009); and (2) uncertainty in the utility location is not communicated to excavator operators in real-time to help them objectively perceive the digging machine’s position relative to the buried utilities (Sterling et al. 2009;CGER - Commission on Geosciences, Environment and Resources Environment and Resources 2000).
The remainder of this paper is organized as follows. The authors first review the current practice and related studies. Following this review, the authors describe the technical details of their methodology in modeling the positional uncertainty of geospatial utility data, monitoring the excavator movement, and synergizing them into a geospatial AR environment for real-time visualization and proximity analysis. The authors then illustrate the test and validation of the newly developed framework. Finally, the authors draw their conclusions and point out future research directions.
This section reviews related studies in modeling the geospatial underground utilities and the associated positional uncertainties, the methods of locating invisible underground utilities, and emerging Augmented Reality-based approaches in visualizing buried, invisible utilities in the context of their spatial context. The current practice of the 811 One-Call procedure and its limitation are also reviewed.
Geographic information systems (GIS) for utility data
Geographic information science (GISci) is the discipline that focuses on understanding the world by describing, analyzing, and explaining human relationships with the earth (Huxhold 1991). GIS is an information system built upon GISci to manage, analyze, and report spatial data, describing phenomena above, on, and underneath the earth’s surface. A GIS is both a database system to manage spatial and non-spatial data and a set of spatial operators for working on spatial relations (Poku and Arditi 2006). Integration of spatial and non-spatial information on a database platform, registration of locations to the real world coordinates, and spatial analytical capabilities are the distinguishing merits of GIS, leading to the proliferation of its applications in civil engineering (Poku and Arditi 2006;Miles and Ho 1999).
GIS allows a utility owner to have a complete utility inventory stored in a single repository that is easy to update and extract (Corbley 2007). Since its emergence, GIS has been steadily replacing paper-based as-built drawings and digital Computer Aided Design and Drafting (CADD) drawings to inventory and manage utility data. It has become the de facto tool of choice for utility owners for creating, organizing and managing geospatial utility information (Sipes 2007). Many utility owners have gone through the transition from paper maps to CADD files or GIS databases via digitization (Cypas et al. 2006).
GIS has been historical two-dimensional (2D), modeling the geometries of objects into georeferenced points, polylines, and polygons. Buried utilities are predominantly modeled as 2D polylines in GIS, missing vertical location information. Some utilities might have their buried depths (e.g. “depth of cover”) stored as attributes and their vertical location might be derived by consulting the reference surface. However, utility depths are rarely referenced to a recognized elevation datum (Federal Highway Administration (FHWA) 1999;Anspach 1995) and any changes of the reference surface make the buried depth a very unreliable source for deriving vertical utility locations.
Locating utilities in the field
When utilities are first installed, their locations are captured in as-built drawings that vary in terms of information richness, positional accuracy, and storage format (e.g. paper-based versus digital). The advancement in tracking technologies such as Global Positioning Systems (GPS) and Radio Frequency Identification (RFID) has greatly facilitated the collection of accurate utility locations for new installations in both horizontal and vertical dimensions (Dziadak et al. 2008;North 2010).
After utilities are installed and covered, the procedure of locating them in the field typically starts with the as-built drawings. Geophysical surveys based on a variety of sensing and locating techniques might be performed to locate buried utilities with good accuracies at the levels of A and B, based on the generally accepted definitions of quality levels in Subsurface Utility Engineering (SUE) (Stevens and Anspach 1993;Lew 1996; American Society of Civil Engineers (ASCE) 2002). Locating techniques include radio frequency (RF) detection techniques, electromagnetic techniques, magnetic methods, vacuum extraction, ground penetrating radar (GPR), and terrain conductivity (Anspach 1995). A number of studies have been conducted to apply the GPR technique in detecting buried utilities (Hereth et al. 2006; National Research Council (NRC) 2000;Butler 2001;Lanka et al. 2001). GPS, though not a detecting technique, has been frequently combined with locating techniques to register the location of detected utilities to real-world spatial referencing system and thus, forms the foundation of integrating with GIS to automate the inventory and update of utility locations in GIS and guide future field location of utilities (Common Ground Alliance (CGA) 2010;Ellis et al. 2009;Manacorda et al. 2007;Bakhtar 2006;Ishikawa et al. 2006;Goldstein 1997).
Uncertainty in geospatial data
Uncertainty has been a major issue in GIS for many years (Heuvelink and Burrough 2002;Goodchild 1998), and is one of the top ten research priorities in GISci (Cobb et al. 2000). Uncertainty can be generally defined as the discrepancy between what a database indicates and what actually exists in the real world (Goodchild 1998) and be described in aspects of positional inaccuracy, errors, vagueness, ambiguity, fuzziness, scale, and sampling (Goodchild 1998;Cobb et al. 2000;Fisher 1999). The literature on the topic makes use of a range of terms related to uncertainty (Crosetto and Tarantola 2001;Duckham et al. 2001), including quality, accuracy, reliability, error, ignorance, precision, clearness, distinctiveness, etc. (Foody 2003). A large amount of research articles have been included in the publications of two international conferences of Symposia on Spatial Accuracy Assessment and Symposia on Spatial Data Quality.
Current practice uses metadata (e.g. data about data) standards such as ISO 19113 (ISO 2001), ISO 19115 (ISO 2003), and Federal Geographic Data Committee (FGDC) (Federal Geographic Data Committee (FGDC) 1998) to measure and record the quality of spatial data upon a common platform (Fisher et al. 2010). Spatial data quality is generally measured in aspects of lineage (e.g. data collection methods and data sources), accuracy, consistency, and completeness as the assessment of results (Fisher et al. 2010). The concept of “fitness for use,” e.g. how well a certain data set meets the needs of an application, has also been proposed as the overall measure of the quality/certainty of geospatial data (Fisher et al. 2010;Devillers et al. 2010).
While metadata provides a common platform for recording and communicating data uncertainty information, it does not provide methods to handle and mitigate the inherent uncertainty in geospatial data. The current practice of GIS is deterministic. All visualization and analyses are being performed as if the underlying GIS data were correct without any incompleteness, errors, and inaccuracy. To cope with this limitation, Burrough (Burrough 1992) discussed an “intelligent GIS” to benefit from available metadata to support the use of uncertain data. Unwin (Unwin 1995) introduced the concept of “error-sensitive GIS” for error management such as data verification and validation, visualization of errors/uncertainties, and simulation and sensitivity analysis to obtain a range of potential results as well as associating a sense of credibility to each scenario. Duchham and McCreadie (Duckhma and McCreadie 20021999) proposed the concept of “error-aware GIS” as an extension to “error-sensitive GIS” by adding techniques to understand errors and integrate errors into decision-making. Devillers et al. (Devillers et al. 2005) designed a prototype multidimensional database system to assist users in assessing the fitness for use of geospatial data, considering errors and error propagation issues.
Error modeling for linear objects
For linear, geospatial utility data, uncertainty is mostly concerned with the positional discrepancy between the records-indicated object locations and their real world locations and thus, uncertainty might be interchangeable with positional error/inaccuracy. Accuracy assessment and error modeling of linear objects have been active research topics. A number of studies have proposed and tested several error models for geospatial linear objects such as roads, utilities, and streams (Mozas and Ariza 2011;Shi and Liu 2000;Goodchild and Hunter 1997;Caspary and Scheuring 1993;Perkal 1956).
Utility lines in GIS are typically modeled as straight line segments that connect two end points. In 2D, the uncertainty of a straight line can be captured as an uncertainty epsilon band that encloses the “true” location of the utility centerline (Mozas and Ariza 2011). This concept was initially proposed by Perkal (Perkal 1956) who used an epsilon band, the 2D space enclosed by two parallel lines that are also tangents to circular errors at the ending points as the probability range for a line. The epsilon band has been discussed frequently in the literature (Blakemore 1984;Aspinall and Pearson 1995) and has been implemented in 2D GIS in various algorithms in the form of a tolerance (Goodchild and Hunter 1997). Caspary and Sheuring (Caspary and Scheuring 1993) and Shi and Liu (Shi and Liu 2000) suggested that intermediate points on a line have smaller errors than the end points and thus, the error band will be “slim” in the middle, leading to a genetic band, or a G-band. Probabilities can be determined for G-bands with various sizes to model the uncertainty in lines (Heuvelink et al. 2007;Wu and Liu 2008). Goodchild and Hunter (Goodchild and Hunter 1997) pointed out that epsilon was often interpreted in a deterministic sense as the minimum buffer width that enclosed the true location of the objects under testing/assessing and was very sensitive to outliers. They proposed a simple buffering approach to evaluate the positional accuracy of linear objects by simultaneously referring to the buffer width and the percentage of lines within this buffer (Goodchild and Hunter 1997). The main limitations of current error modeling for linear objects are (1) being 2D, (2) being deterministic (e.g. the epsilon band is determined to enclose the true location and given the existence of outliers, the band width is unreasonably large), and (3) the lack of a method to estimate the most probable location of linear objects given their recorded location and associated positional uncertainty, the reverse procedure of accuracy assessment.
Augmented reality (AR) for utility visualization
A relatively new technological advancement in AR has enabled the visualization of invisible, buried utilities in the context of their real-world surroundings (Kamat 2003;Behzadan 2008;Roberts et al. 2002a). In an AR environment, buried utilities can be visualized as floating lines on the correct locations relative to background images and photos. If three-dimensional (3D) reference data is available, the buried utilities can be offset downward to compose an interactive 3D display (Talmaki and Kamat 2012). The positional uncertainties associated with geospatial utility data might be visualized as 3D buffers/”halos” to provide an uncertainty-aware visualization and proximity analysis (Talmaki et al. 2012).
Current practice of the One-call system
Recognizing excavation damage as the largest single cause of pipeline accidents and associated deaths and injuries, the National Transportation Safety Board (NTSB) initiated the development of the One-Call (811) notification system in 1970s (National Transportation Safety Board (NTSB) 1998). Before digging, it is required by Federal Law to have the location of all buried utilities in the vicinity of the excavation area to be pre-marked. Excavation contractors are required to contact state-wide one-call agencies 48 to 72 hours prior to the start of the operations. One-call agencies, in turn contact their member companies with the location of the excavation site. If the member companies determine an overlap between the job site and their utility lines’ location, they mark the location of the utility lines using spray paint, flags, stakes or any combination of these.
The markings are typically made referring to data from as-built drawings. An obvious limitation of this one-call procedure is that the markings are typically the very first things being removed when excavation starts. The excavation operators then have to rely on their memory to estimate the utility location and their imagination to compose mental images of the proximity of the digging implement to utilities and how the excavation operation might interact with utilities. This is an error-prone procedure that in turn, is the main reason of the large number of utility strikes even after the one-call procedure is followed.
The overarching goal of this study is to prevent unintended collisions between a digging implement and buried utilities via uncertainty-aware visualization and proximity analysis of excavation operations in a virtual environment. The premises for the research study presented in this paper are that (1) increased spatial awareness of excavator operators (e.g. being able to “see” the buried utilities and the movement of the excavator bucket and judge the proximity between utilities and the bucket) leads to improved excavation safety, and (2) such an increased in spatial awareness can be achieved through the combination of visual perception and analytical proximity monitoring of excavation operations. Considering the pivotal role of data quality and the tremendous uncertainty in utility locations, the research hypothesis is that uncertainty, particularly spatial uncertainty, can be brought into the decision-making process in an easy-to-understand manner for both visualization and analytical proximity monitoring to prevent unintended collisions and increase excavation safety.
To achieve the aforementioned goal, the authors designed a framework (Figure 1) to synergistically incorporate inherent uncertainties associated with geospatial utility data into a geospatial AR environment for error-aware visualization and proximity monitoring. This section describes the technical details in modeling error/uncertainty in geospatial utility data and quantifying the error model by linking accuracy to data lineage. This section also presents an error-aware utility data model in the format of Universal Modeling Language (UML) and its implementation of Extensible Markup Language or XML (Cypas et al. 2006) model for data transfer and sharing with downstream visualization and analysis, given its flexibility, extensibility, and compatibility with open-source requirements. The mechanism of proximity monitoring and uncertainty-aware reasoning is also provided as the base for appropriate warning messages.
Uncertainty modeling of geospatial utility data
The authors modeled utility lines as 3D straight line segments connected at turning points (Talmaki et al. 2012). In practice, the 3D locations of turning points are first obtained and 3D straight lines can be constructed by connecting these 3D turning points. Of all positional accuracy/error models, the most related are those that apply to points and lines. Uncertainty, when expressed as probabilities associated with geospatial extents that contain the “true” location of that object, can be derived from the recorded utility location and its positional accuracy.
The horizontal probability circles and the vertical linear probability ranges can be combined into 3D probability ellipses/spheres (Greenwalt and Shultz 1968) or probability cylinders. A probability cylinder is constructed by taking a probability circle and extruding it vertically. The resulting probability is the product of the corresponding circle probability and the linear probability. For instance, the horizontal 50% circle is extruded to reach the vertical 90% linear probability, resulting in a 45% probability cylinder. Unlike probability ellipse/circle, many cylinders can have the same probability, making implementation impractical. A random simulation was conducted, of which the results confirmed that 3D ellipsoids closely follow the point clouds and thus, were chosen to model the uncertainty of utility turning points.
The function f indicates that radius is dependent on the location along the centerline, the probability of interest, and the data lineage. The determining effect of the data lineage on uncertainty models will be described detail in the section of Construction of Uncertainty Models for Geospatial Utility Data. When the locations of utility lines are collected directly such as in GPR, the 3D Probability G-band can be simplified into 3D Probability Bands that can be derived via the 3D buffering approach. Rather than being “slim” in the middle, a probability band is of uniform size along the line.
Validation of uncertainty models
Construction of uncertainty models for geospatial utility data
The authors constructed uncertainty models by quantifying the 3D Probability bands in pairs of probability and band size. The probability expressed in percentage refers to the probability of a specific 3D geospatial volume determined by a particular band size enclosing the “true” location. The band size was further detailed via a mathematical function that describes the shape and extent of the 3D G-band. The function might be simplified into a cylinder function when describing a 3D Probability band that does not “slim” in the middle along the line.
Quality Groups of Utility Location Data
Precise horizontal and vertical location obtained by actual exposure and measurement.
Location information obtained through the application of surface geophysical methods
Location information obtained by inferring from above-ground utility features.
Location information derived from existing records or oral recollections.
Uncertainty-aware geospatial data model for utilities
Geospatial augmented reality environment for visualization and proximity analysis
In order to visualize uncertainty-aware geospatial data in Augmented Reality, the data must first be converted to a format suitable for 3D graphical visualization. More importantly, the geodata accuracy and its uncertainty must be graphically characterized and displayed for it to be useful in excavator operation and control. This research developed methods to characterize utility geodata in terms of its lineage and accuracy. Together, the accuracy and lineage help characterize the errors of a geodata set and the reliability that can be associated with its source. This information can be usefully exploited during excavation by displaying not only the expected locations of utilities to an operator, but also the degree of uncertainty (or “buffer”) associated with the expected locations in the form of a “halo”. In a 2D projection, the buffer calculated by interpreting the geodata’s lineage and accuracy is represented as a “band” whose width represents the uncertainty associated with the utility’s location. In a 3D projection, the buffer is identified by increasing the diameter of the cylindrical geometry representing the utility line.
Interpretation of System Level Uncertainty in Proximity
At least a 60% probability the bucket is at least d (1 m) away from the utility
At most a 40% probability the bucket is at most d (1 m) away from the utility
At least an 85% probability the bucket will NOT hit the utility
At most an 15% probability the bucket WILL hit the utility
The authors validated the developed geospatial uncertainty models in a controlled environment. Monte Carlo simulations for validating the uncertainty models for points and lines were described in an earlier section and therefore, will not be repeated here. The technical feasibility of the proposed geospatial AR ideas was evaluated by implementing a visualization and proximity analysis framework designed to visualize “error-aware” subsurface utilities during ongoing excavation operations for improved context awareness and accident avoidance.
The data used in the experiments was provided by DTE Energy, which is the largest provider of electricity and gas in southeast Michigan and by consequence owns significant underground distribution assets. The first step in pre-processing the data was assigning a specific lineage to chosen geospatial data sets. A data set assumed to have been recorded following a Ground-Penetrating Radar (GPR) survey was selected for subsequent analysis. The uncertainty model was chosen next. Given the data lineage, a cylindrical uncertainty model was chosen with quantitative error magnitudes in both horizontal and vertical directions. The error characterized data set was then archived in the developed XML schema.
The current practice of excavation damage prevention followed by state one-call centers, and the limitations in current practice were noted in an earlier section. Geospatial Augmented Reality can help to accurately visualize a proposed excavation area and digitize the located underground utilities, thus helping bridge the communication gaps among excavation contractors, one-call centers, utility owners, field locators and excavator operators. First, a contractor can issue a “visual” ticket to the one-call center and utility owners by superimposing a semi-transparent layer above a proposed excavation area. This in turn enables dispatched locators to come to the field, “see” the proposed excavation area (Figure 10), and precisely mark the surveyed area.
The following procedure was adopted to interpret error-aware geospatial data files and build conduit (e.g. pipe) models in the augmented space. First, the spatial and attribute information of pipelines was extracted by parsing the data model. For example, the geographical location of pipelines is recorded under the Geometry element as “LineString”. A cursor was designed to iterate through the XML file, locate “LineString” elements, and extract the geographical locations. Second, consecutive vertices within one “LineString” were converted from the geographical coordinate to the local coordinate in order to raise computational efficiency during the registration routine. The first vertex on the line string was chosen as the origin of the local coordinate system, and the local coordinates of the remaining vertices were determined by calculating the relative 3D vector between the rest of the vertices and the first one, using the Vincenty algorithm (Vincenty 1975). In order to save memory, a unit cylinder is shared by all pipe segments as primitive geometry upon which the transformation matrix is built.
Third, the primitive cylinder geometry was scaled, rotated, and translated to the correct size, attitude, and position. For simplicity, the normalized vector between two successive vertices was named as the pipeline vector. First, the primitive cylinder was scaled along the X- and Y-axis by the radius of the true pipeline, and then scaled along the Z-axis by the distance between two successive vertices. In addition, the scaled cylinder was rotated along the axis—formed by the cross product between vector <0, 0, 1> and the pipeline vector—by the angle of the dot product between vector <0, 0, 1> and the pipeline vector. Finally, the center of the rotated pipeline was translated to the midpoint between two successive vertices. This step was applied to each pair of two successive vertices to extract the complete geospatial data set.
This paper described a dynamic approach to incorporate the uncertainties associated with buried utilities data into a geospatial-AR system for real time visualization and proximity analysis. This research modeled uncertainties of buried utilities data as probability bands, described by pairs of band size and the probability of the 3D space constructed by “buffering” at the band size to enclose the “true” location of utilities. Given the research hypothesis of that positional uncertainty of utilities data is dependent on data lineage, e.g. the genesis and processes used to collect and interpret data, the positional uncertainty of utilities data was derived in real time by referring to the data lineage model. Consequently, not only the 3D shapes and locations of utility lines and vertices, but also the associated uncertainties could be visualized, as 3D probability bands in a geospatial AR environment. This newly created approach is expected to contribute to the safety in urban excavation via the integration of Geoinformatics and construction informatics in real time in an uncertainty-aware manner.
A framework, a generic data model, and a sample XML implementation of the data model were developed and tested in this study. The impacts of the uncertainties in the utilities data on proximity analysis, e.g. analyzing the closeness between a digging implement and the underground utilities, were also discussed. A method was also developed for analyzing the proximity and interpreting the results in the context of uncertainties that could come from both the utilities and the excavator movement. Visualizing the uncertainty associated with utility location data and appropriately interpreting the resulting proximity were found to be key elements in any visual and analytical guidance furnished to excavator operators or field personnel to prevent utility strikes. It was found that uncertainty-aware, geospatial AR was the enabling technology to bring the interaction between a digging implement and the buried utilities to the excavator operator both visually and analytically (with appropriate interpretations). It was also found that having a practical object-oriented, open access, and uncertainty-aware utility data model was critical to the sharing of utilities data and its inherent uncertainties with downstream applications. Such a data model could also serve as the base for management and sharing of uncertainty-aware utilities data from a life cycle perspective.
The authors’ future goal in this uncertainty-aware, geospatial AR direction is to create advanced uncertainty qualifying and quantifying algorithms, and the further integration of technologies such as GPR and machine control and guidance (MAC).
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