Seabed Mapping and Inspection 2011, February 9-11, Geilo, Norway EIVA A/S Page 1 of 13 S-CAN in NaviModel3 Efficient Sonar Data Cleaning Implementation of the S-CAN Automatic Cleaning algorithm in EIVAs NaviModel3 By Lars Dall, Survey Manager, EIVA A/S Abstract The optimization of the post-processing environment within EIVAs 3D modelling tool, NaviModel3 has focused on two aspects primarily: Optimization of the visual environment in order to supply the operator with enhanced and improved background information for his decision making Speed-optimization and automation of the entire post-processing task One of the major components in the speed-optimization and automation has been the implementation of the S-CAN automatic data cleaning algorithm. Key words: Data modelling, optimization and practical efficiency, manual and automatic cleaning functionalities, performance 1. Introduction The objective of the optimization of the post-processing environment within NaviModel3 has been to supply the users with tools that facilitate the production of better and more unambiguous bathymetric data, faster and with reduced user intervention. For the optimization of the visual environment, a series of new features have been implemented. In the development phase of NaviModel3, it was regarded an important enhancement to the visual environment to be capable of displaying all data of relevance in an integrated 3D based DTM- window with video functionalities being integrated into the visualization of the data. Of specific interest in connection with pipeline inspection tasks is furthermore elements that will improve the visualization of the pipe object. Also the speed-optimisation and automation has had a series of focus areas. The fact that NaviModel3 has an unlimited model-size is expected to be a significant factor. Furthermore, with seabed mapping and investigations, such as pipe-line inspection, to a large extent depending on reliable and accurate seabed information, some kind of fast, consistent, yet user-friendly, automatic cleaning of the seabed scanning data has been regarded a necessity. The implementation of the S- CAN automatic cleaning algorithm is the response to this. In this context, it is worth noting, that cleaning can only take place within the framework of NaviModel3, i.e. in the modelling phase rather than in the editing phase of the post-processing, due to the fact that the raw observations are inherent in the terrain model. This introduces a new approach to the cleaning of multi-beam based bathymetric data that gives the user the possibility to observe the consequences of the cleaning, be it automatic or manual, instantly. The objective of this paper is thus to describe the environment in which the S-CAN cleaning algorithm has been implemented, to describe the actual implementation, and to give some estimates on the efficiency of the cleaning, mainly in order to substantiate that the objectives of the optimization have been met. The paper will furthermore consider potential future developments and enhancements to the automatic cleaning functionalities.
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Seabed Mapping and Inspection 2011, February 9-11, Geilo, Norway
EIVA A/S Page 1 of 13 S-CAN in NaviModel3
Efficient Sonar Data Cleaning Implementation of the S-CAN Automatic Cleaning algorithm in EIVAs NaviModel3
By Lars Dall, Survey Manager, EIVA A/S
Abstract
The optimization of the post-processing environment within EIVAs 3D modelling tool,
NaviModel3 has focused on two aspects primarily:
Optimization of the visual environment in order to supply the operator with enhanced and
improved background information for his decision making
Speed-optimization and automation of the entire post-processing task
One of the major components in the speed-optimization and automation has been the
implementation of the S-CAN automatic data cleaning algorithm.
Key words: Data modelling, optimization and practical efficiency, manual and automatic
cleaning functionalities, performance
1. Introduction
The objective of the optimization of the post-processing environment within NaviModel3 has been
to supply the users with tools that facilitate the production of better and more unambiguous
bathymetric data, faster and with reduced user intervention.
For the optimization of the visual environment, a series of new features have been implemented. In
the development phase of NaviModel3, it was regarded an important enhancement to the visual
environment to be capable of displaying all data of relevance in an integrated 3D based DTM-
window with video functionalities being integrated into the visualization of the data. Of specific
interest in connection with pipeline inspection tasks is furthermore elements that will improve the
visualization of the pipe object.
Also the speed-optimisation and automation has had a series of focus areas. The fact that
NaviModel3 has an unlimited model-size is expected to be a significant factor. Furthermore, with
seabed mapping and investigations, such as pipe-line inspection, to a large extent depending on
reliable and accurate seabed information, some kind of fast, consistent, yet user-friendly, automatic
cleaning of the seabed scanning data has been regarded a necessity. The implementation of the S-
CAN automatic cleaning algorithm is the response to this.
In this context, it is worth noting, that cleaning can only take place within the framework of
NaviModel3, i.e. in the modelling phase rather than in the editing phase of the post-processing, due
to the fact that the raw observations are inherent in the terrain model. This introduces a new
approach to the cleaning of multi-beam based bathymetric data that gives the user the possibility to
observe the consequences of the cleaning, be it automatic or manual, instantly.
The objective of this paper is thus to describe the environment in which the S-CAN cleaning
algorithm has been implemented, to describe the actual implementation, and to give some estimates
on the efficiency of the cleaning, mainly in order to substantiate that the objectives of the
optimization have been met. The paper will furthermore consider potential future developments and
enhancements to the automatic cleaning functionalities.
Seabed Mapping and Inspection 2011, February 9-11, Geilo, Norway
EIVA A/S Page 2 of 13 S-CAN in NaviModel3
2. NaviModel3
The NaviModel3 DTM modelling software is a tool for the generation of and manipulation with
Digital Terrain Models (DTMs) on the basis of either single- or multi-beam based bathymetric data.
The modelling is founded on either Triangular Regular Network (TRN) or on Triangular Irregular
Network (TIN) algorithms. The TRN geometry type models consist of equally spaced triangular
cells, whereas for the TIN geometry type models, triangles are based on the raw data.
A series of dedicated add-on modules that are designed for specific tasks, have been developed for
inclusion in the modelling tool. These comprise:
Online 3D module. This module facilitates visualization in an online environment in which
various objects can be shown in real time and superimposed onto a DTM
Catenary module that facilitates a variety of catenary based tools, as well as calculations and
visualizations associated with pipe- and cable-laying jobs
Pipeline inspection module
Figure 1 The NaviModel3 Post-processing environment
Figure 1 shows the flow for a typical NaviModel3-based post-processing task. Input data is
originating from the on-line and editing phases. Furthermore video- and event-information,
originating from external sources, can be imported. NaviModel3 hosts different tools, for cleaning,
visualization, determination of the pipe etc., that are used in combination with external utilities for
offline eventing and automatic cleaning. Ultimately, in order to make the bathymetric data available
for further processing and documentation, a series of generic and predefined exporting
functionalities have been implemented.
3. The Quad Tree Principle
Within NaviModel3, data is, in general terms, organised in a so-called Quad Tree structure. A Quad
Tree is a tree-based data structure in which each internal node has up to four children. Quad Trees
are commonly used to partition a two dimensional space by recursively subdividing each level into
four quadrants or regions as visualised in the figure below. In a Quad Tree, records are stored in
locations called leaves. The name originates from the fact that records always exist at end points;
there is nothing beyond them. The 1st level is also sometimes termed the root. Branch points, on the