things to keep in mind while scanning get excellent data to ......things to keep in mind while...

Things to keep in mind while scanning-

Get excellent data to work with

Richard SteffenCTO

Martin GranerR&D Engineer

Things to keep in mind - 2020

Table of contents

1. Project planning2. Scanning3. Registration

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Table of contents

1. Project planninga. Requirementsb. Time x moneyc. Planning

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What does the customer want / need:

● Accuracy and point density● Point cloud for presentation● Processing for BIM / CAD

Project planning - Requirements

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On accuracy:

● Global vs local accuracy● Verification


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One scan


Two scans

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● Scanning with / without colour● Resolution● Estimated Scan positions● Data accuracy - mandatory things to achieve it

Project planning - Time x money

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TODO: Übersichtstabelle mit Scanning zeiten je auflösung…

https://docs.google.com/spreadsheets/d/1mJyidKomhSUzHRKro8PFIrJw-IO1Gq7W9At6vDOW3q4/edit#gid=1535648332


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Beam Divergence Spot Size

Distance ----------------------------------------------- >



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Single Point Accuracy

Distance ----------------------------------------------- >

ςα

ςd

ςα = const (Faro 19 arcsec = 0.005°)

ςd = const + ppm (Faro 1mm + 10 ppm)



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Why beam divergence and single point accuracy is important?

● Beam divergence○ Reflected waveform has multiple peaks in time diagramm○ Usually mean will be computed (ghost points on edges)

● Single point Accuracy○ Should be considered in the adjustment○ Should be considered in distance measurements

Project planning - Planning

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● Use existing Layout plan! (Scan position planning & field book)● Loop closures, feature placements● Geodetics● Use high standard spheres and checkerboard targets● Do a site inspection prior to scanning● On-site registration


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Table of contents

1. Project planning2. Scanning

a. C2C vs feature basedb. Georeferencingc. How to scan

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Scanning - C2C vs feature based

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C2C Feature based

Matching via: ● Point to Point (ICP)● Point to Plane (ICP*)● Planes (patches)

● Spheres● Checkerboard targets● Natural features

Limits: ● No planes/normals in three axis existing (e.g. long hallways)

● Not enough features (<3)● Bad feature constellation

Gets time consuming when:

● No approximation values (Creating the connection graph)Manual Topview pre-alignment

● Bad Automatic feature extraction● Getting wrong approximation

values

Constellation search: ● Scan to scan connections ● Global Scan connections


● Is it necessary?● Direct vs indirect● While registering vs after registration

Scanning - Georeferencing

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● Scan position connections● Loop closure● Feature distribution● How to scan through a door● Automatic sphere detection

Scanning - How to scan

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Scan:

Scan connection



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Scan:

Scan connection



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Loop closure

Scan:



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Loop closure

Scan:



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Feature distribution

Feature:

Scan:



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The good

Feature:

Scan:



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The bad

Feature:

Scan:



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The ugly

Feature:

Scan:



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The super bad luck

60°

60° 60°

Feature:

Scan:



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The super bad luck

Feature:

Scan:



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The super bad luck

Feature:

Scan:



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The super bad luck

Feature:

Scan:



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Door scanningConfiguration 1



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Door scanningConfiguration 2


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Source: https://www.laserscanning-europe.com/en/news/use-laser-scanner-reference-spheres-optimal-distance-to-the-scanner

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Real life sphere detection in Faro Scene

https://www.laserscanning-europe.com/en/news/use-laser-scanner-reference-spheres-optimal-distance-to-the-scanner


Table of contents

1. Project planning2. Scanning3. Registration

a. Feature basedb. Cloud to Cloud (C2C)

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The big challenge

● Determine the connection graph○ Which Scans are overlapped (adjacency matrix)○ Which local features correspond to each other (constellation search)

● Simplify automatic search algorithms○ Complexity O (n2 / 2)○ Clustering 20->100 Scans per cluster

Registration

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● Wording issues: “Bundle adjustment” - It’s Photogrammetry, not scanning because of the 3D->2D Projection (bundle of rays)

● Least squares adjustment -> Squared sum of the residuals (errors) will be minimized

Registration

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Feature based model

● Each Scan depends on H (X, Y, Z, ⍵, φ, ϰ) - rigid motion● Model for point based features: Xw = Hw Xlocal

○ Xw Unknown 3D Position○ Xlocal Measured local 3D Position

● Georefernce Observation○ Xw = Xgeod

● Inclinometer Observation⍵ = ⍵inc , ϰ = ϰinc

Registration - Feature based

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Global Adjustment - Feature Based

● All “raw” observations with their accuracy introduced in one big adjustment (usually sparse system)

● All errors are distributed into the whole network● For all observation types a posteriori accuracy can be estimated● Full error analyze possible● Georeferencing included, reduce drift effect

Registration

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“Two step” alignment - Used very often for Cloud2Cloud

● 1. Step: Alignment of relative orientation between two scans○ The model: X2 = ΔH12 X1 (Helmert transformation between 2 scans)○ Cloud 2 Cloud use IPC to detemine ΔH12

● 2. Step: Net adjustment of all relative orientations in a cluster or for complete overlapping graph

○ The model: Hw2 = ΔH12 Hw1 (Relative orientation)

● 3. Step: Cluster alignment (big residual stress on cluster bounds)● 4. Step: Georeferencing (big residual stress)

Registration - Cloud to Cloud

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Recommendations for the registration

1. Use Cloud2Cloud for small projects (20-50 scan) and no georeferencing

2. Use Feature based Registration for proof of result

3. Introduce high accuracy control points (GPS/Total station) for Georeferencing and drift compensation

Registration - Cloud to Cloud

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