Vertical surface processing from integrated MBES – lidar data

Vertical surface processing from integrated MBES –lidar data

Eli Leblanc, Mathieu Rondeau & Jean-Guy Nistad

CARIS 2012

Introduction

• In the last few years, CIDCO has been developing expertise in port infrastructure inspection

– Current method: subjective and partial observations from divers

– CIDCO method: objective, full coverage and efficient sonar-lidar 3D dataset

Introduction

Introduction

• For the first time, port management bodies have a full view of the submerged part of their structures

• Precious information tobetter plan maintenanceand repair

Introduction

• 3D points cloud is not easily usable by engineers

• Uncertainty on the sonar 3D dataset exceeds the clients specifications of 5 cm

• Vertical surfaceprocessing is not yetsupported in CARIS HIPS

Introduction

• 3 solutions were tested1) Vessel file roll bias (HIPS)

2) Inverse-distance WMA filter (HIPS-Matlab)

3) XTF files rotation (Matlab-HIPS)

Dataset

• Port of Montréal• November 2011• 30° tilted MBES Reson Seabat 7125 • Data recorded in xtf format

Dataset

GPS

LiDARScanner

Camera

IMU

Reson 7125 SV30° starboard tilted

ALMIS 350

Dataset

1) Vessel file roll bias

• 90° roll bias applied to the vessel file during merging → HIPS

• BASE surface → HIPS


• Original sonar data (observed depths)


• Step 1: 90° roll bias in the vessel file


• Step 1: Sound velocity correction


• Step : Merge the line (processed depths)


• Step 3: Merge the line


• Step 4: Remove the seabed


• Step 5: Create a BASE surface– Swath angle, 10 cm


• Limits– Roll bias applied in sonar referential at each

ping rather than at a fixed rotation point• Distortion• Impossible to process multiple lines

2) Inverse-distance WMA filter

• Data merging → HIPS

• Filtering and surface meshing → Matlab


• Step 1: Export merged data from HIPS and load them in Matlab

• Step 2: Remove the seabed (depth threshold)


• Step 2: Model the infrastructure axis and rotate around z


θaxis

y

x


• Inverse-distance weight moving average filter (10 cm, r = 14.14 cm)

Infrastructure axis (m)

Alti

tude

(m

)

20 40 60 80 100 120 140

-6-4-20

-0.500.5


Alti

tude

(m

)

65 70 75 80 85 90 95 100 105 110 115

-6

-4

-2

0

-0.5

0

0.5


• Limits of the method– Costly in computation time

• 4 lines = 150 m = 750 000 soundings = 30 minutes

– Possible memory problems for larger datasets– No shadow effects or texture


Alti

tude

(m

)

65 70 75 80 85 90 95 100 105 110 115

-6

-4

-2

0

-0.5

0

0.5

3) XTF files rotation

• 90° rotation applied on xtf files → Matlab

• Data cleaning, merging and BASE surface → HIPS

• Step 1: Extract fields from xtf files– For each swath

• x-y coordinates• Pitch, roll, heave,

heading

– For each sounding• Two-way travel time• Angle


(x,y)z

yx

(t,θ)

θ t

• Step 2: Model the infrastructure axis and rotate around z


θaxis

θheading θ'heading

axisheadingheading '

y

x

• Step 3: Compute range


2

ctR

z

y

x

heav

e

(x,y)sonar

(t,θ)sounding

θ

R


• Step 4: Project (x,y)sonar across swathz

y

x

heav

e

(x,y)sonar

(x,y,z)sounding

θ

sonarsounding xx

sinRysounding

heaveRz cos R

• Step 5: Rotate around x (90°)


z

yx

yz

zy

z

yx

90°

• Step 6: Edit θ


z

y

x

θ'

θ

)90('

• Step 7: Edit navigation - (x,y)sonar

– x coordinates → infrastructure axis


y'

x'


– y coordinates


90°

y

zx

z

yx

sonaryheave '


– y coordinates


(x,y)’sonar

''' sinRyy soundingsonar

z

y

x

R(x,y,z)’sounding

θ'

θ heaveRz

zy sounding

cos

'

• Step 8: Edit attitude angles (Pitch)– x-z plane– x to horizontal axis


z

yx

z

xy

θpitch



90°

z

xy

z

xy

θpitch



z

xy

z x

y

θpitch

θ’heading

pitchheading 90'

• Step 8: Edit attitude angles (Heading)– x-y plane– y to horizontal axis


y

z x

y

z x

θheading



90°

θheading

y

z x

y

z x



z

y x

θheading

θ'pitch

y

z x

)90(' headingpitch

• Step 8: Edit attitude angles (roll)– y-z plane– z to vertical axis


z

yx

z

yx

θroll

• Step 8: Edit attitude angles (roll)– y-z plane– z to vertical axis

z

yx

θroll


z

yx

θ'roll

rollroll '

90°

• Step 9: Edit fields to create new xtf files– For each swath

• x-y coordinates• Pitch, heave, heading

– For each sounding• Beam angle



• Step 10: Create a BASE surface in HIPS– Swath angle, 10 cm


• Limits of the method– Sound velocity cannot be corrected in HIPS

• SVC in Matlab would imply correcting for attitude

– TPU would have to be investigated to use CUBE

Discussion

• Comparison of methodsMethod 90° roll bias WMA filter XTF rotation

Discussion


Complexity of implementation Low Medium High

Discussion



Processing time Low Very high Medium

Discussion




Distorsion Yes No No

Discussion





SVC Yes Yes No

Discussion





SVC Yes Yes No

Multiple lines No Yes Yes

Discussion





SVC Yes Yes No


Detailed view Yes No Yes

Discussion





SVC Yes Yes No



Texture Yes No Yes

Discussion





SVC Yes Yes No



Texture Yes No Yes

CUBE Yes No No

Discussion

• Comparison of methods

90° roll bias


Alti

tude

(m

)

30 40 50 60 70 80

-6

-4

-2

0

-0.5

0

0.5

WMA filter

XTF rotation

Conclusion

• 3 methods were tested to process vertical surfaces– 1) Vessel file roll bias (HIPS)

• Distorsion makes it unusable

– 2) Inverse-distance WMA filter (HIPS-Matlab)• Long to process• Less details and no texture

– 3) XTF files rotation (Matlab-HIPS)• Promising• Needs further development (SVC,CUBE,LAS)

Questions?

Vertical surface processing from integrated MBES – lidar data

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