New possibilities of remote sensing, point clouds, laser ... · 20/11/2015 1 New possibilities of remote sensing, point clouds, laser scanning and UAV LS Juha Hyyppä Director, NLS/FGI,

20/11/2015

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New possibilities of remote

sensing, point clouds, laser

scanning and UAV LS

Juha Hyyppä

Director, NLS/FGI, [email protected]

Director, CoE-LaSR

Prof., NLS/FGI, [email protected]

Content

• What is NLS/FGI?

• General Principles in Laser Scanning (LS)

• 3D Remote Sensing Comparison

• Autonomous driving & Information Systems & Decision

support systems

• Mini-UAV LS

• Personal and Mobile LS

• Plot level point clouds

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mailto:[email protected]

mailto:[email protected]

20/11/2015

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FGI • Research institute in National Land Survey of Finland (since 1.

Jan 2015)

• FGI was the most productive research environment in Finland (output/input)

• About 110 Staff (roughly 90 scientist)

• Budget roughly 10 MEU (mostly outside financed)

• Highly academic institute (nearly 50% staff has PhD)

• Publish around 60 ISI Web of Science peer reviewed articles (200 peer reviewed articles)

• Three joint professorships with universities

• The only Finnish research institute that coordinates Center of Excellence by Academy of Finland

Contribution to NLS Topographic Database

Crowdsourcing

Courtesy to JP Virtanen & J. Hyyppä, Aalto/FGI

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Key Research Areas

1. Reference Systems • National coordinate, height and gravity

systems

• Nummela standard baseline and Metsähovi research station

• Deformation networks (Nuclear safety)

• Photogrammetric and laser scanning test fields

2. Changing Earth • Land use change

• Land uplift and deformation

• Multitemporal remote sensing data analysis

• Global gravity change

• safety and environmental aspects

• Change of land topography

• Vegetation

3. Mobile Geomatics • Mobile mapping

• GNSS technologies

• Indoor – outdoor navigation

• smart mobile geospatial solutions

• Mobile GIS

4. Spatial Data Infrastructures • Interoperable, standardized data services

• Harmonization and quality of geospatial data

• Network-based processing services

• Use and usability of GI-data and applications

• Visualization and cartographic presentation

-4 -3 -2 -1 0 1 2 3-3

-2

-1

0

1

2

3

4

5

Itä (m)

Pohjo

inen (

m)

GNSS

PPP-RTK

Mobile precise positioning with FinnRef

FinnRef

P3 service project

Users

Positioning

accuracy

targeting 0.5 m

Intelligent

transportation

systems

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Local deformations

3D Geographic Information for Hikers

In the MenoMaps - project a mobile map service for the hikers has been developed.

Department of Geoinformatics and Cartography

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Forest map

Courtesy to J. Oksanen et al. FGI

Wintermap


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https://youtu.be/lNwdSmQV_Fo


Centre of Excellence

in Laser Scanning Research:

“Together what is otherwise impossible”

Pulsed time-

of-flight laser

radar

Juha Kostamovaara

Univ. Oulu

Laser scanning

for precision

forestry

Markus Holopainen

Univ. Helsinki

Mobile and

ubiquitous

Laser

Scanning

Juha Hyyppä

FGI

Laser scanning

for built

environment

Hannu Hyyppä

Aalto Univ.

Hardware-driven approach

International benchmarking studies Courtesy to J. Hyyppä. FGI

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Development of new sensors and test

their feasibility for various applications

Courtesy to A. Jaakkola, FGI

Solid Potato Oy – Your partner in R&D

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General Principles in Laser

Scanning

Fundamentals of multiple discrete return

technology

3rd return

from ground

1st return

from tree top

2nd return

from branches 1st (and only)

return from

ground

Courtesy to Leica

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ALS Output

Output: x,y,z, intensity !

Courtesy to Hannu Hyyppä, Petri Rönnholm,Aalto

Platforms for Laser Scanning

TLS

Terrestrial Laser Scanning

MLS

Mobile Laser Scanning

ALS

Airborne Laser Scanning

Courtesy to Hyyppä, Kukko, Kaartinen, Jaakkola FGI

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Characteristics of LS

Airborne Laser Scanning

• Point density 0.5-40 pts/m2

• Homogenous point density

• Elevation accuracy 5-30 cm

• Planimetric accuracy 20-80 cm

• Few hundred m to several km

• Transfer costs

• Cost-effective for areas larger than 50km2

• Multitemporal ALS applications studied relatively little (Murakami et al. 1999, Yu et al. 2004)

• Reference book Shan and Toth (2009), Vosselman and Maas (2009)

Airborne mini-UAV scanning

• New research area (idea in Zhao

et al., 2006, first demo for

mapping Jaakkola et al. 2010)

• Point density 10-several hundred

pts/m2

• Elevation accuracy 5-30 cm

• Altitude tens of m

• Usable for research and

multitemporal studies

• Presently commercial applications

• Reduces CO2 consumption of

research

Courtesy to J. Hyyppä, FGI

Characteristics of LS

Mobile laser scanning

• Point density in the range of 100 to several thousands pts/m2

• More homogeneous (TLS) point density

• Point accuracy of few centimeters (egg) when collected with good GNSS coverage

• Applicable range of few tens of m

• Viewing angle different from ALS

• Higher variation in the range data (e.g. round traffic poles are not round in the data)

• Vast data sets when data collection is done continuously

• Need for automatic and interactive algorithms

Terrestrial Laser Scanning

• Point density in the range of 10000 pts/m2 at the 10 m distance

• Distance accuracy of few mm to 1-2 cm

• Operational scanning range from 1 to several hundred m

• Feasible for small areas less than few tens of m distance

• Processing time challenging: image processing technigues applied

• Small variation in data, e.g. Distance variation low -> surface normal etc can be calculated

• Reference book: Vosselman and Maas (2009)

Courtesy to J. Hyyppä, FGI

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MLS Point Cloud Processing 1/2

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Attribute

Extraction

Classification of

Segments

Object

Location

Prediction

Point Segments

Unorganised 3D Point Cloud

Predicted Object locations

Segmentation Removal of

Ground and

Facades

Pre-

Processing

Courtesy to M. Lehtomäki, FGI

MLS Point Cloud Processing 2/2

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Point Cloud Ground Removed

Segmented/clustered Classified Segments

Tree

Lamp post

Traffic sign

Car

Pedestrian

Undefined

Courtesy to M. Lehtomäki, FGI

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Matikainen, L., Hyyppä, J., and H.

Hyyppä, 2003. Automatic detection of

buildings from laser scanner data for

map updating. Workshop '3-D

reconstruction from airborne

laserscanner and InSAR data', Dresden,

Germany 8-10 October 2003. The

International Archives of

Photogrammetry, Remote Sensing and

Spatial Information Sciences, Vol.

XXXIV, Part3/WG13, 218-224.

OK

New

Not found

Classified as

tree

First publication in using

LS in map updating

Courtesy to L. Matikainen, FGI

3D Remote Sensing

Comparison

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DTM, DSM, nDSM

DSM

DEM, DTM

CHM, nDSM

Courtesy to St-Onge

25x25 m

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2x2 m

Interpreting ALS Point Clouds

DTM

DSM

nDSM

Crown Volume

Near-Surface Vegetation

Tree Stems

Courtesy to TU Wien

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Utilizing individual

tree information in

laser assisted forest

inventory

Point clouds – future of

forest resource

assessment

3D Remote Sensing comparison

• Airborne and Spaceborne systems, 90 plots

• Helicopter LS & Tomoradar (tbd)

• ALS (2 point densities)

• ALS Full Waveform (Finnmap)

• NLS open data 2012

• Airborne images

• ALS data

• SAR

• Tandem X interferometry 2012-2013

• TerraSAR X Radargrammetry

• Worldview Stereo

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Relative RMSE for forest attribute estimates using area-based approach and random

forest based on various RS 3D data and 91 sample plots of 32 m x 32 m size.

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0,00

5,00

10,00

15,00

20,00

25,00

30,00

35,00

ALS-900m ALS-2500m ALS-NLS AP WV2 TSX TDX

RM

SE

(%

) Mean height (m)

Mean DBH (cm)

Basal area (m2/ha)

Volume (m3/ha)

Biomass (Mg/ha)

Courtesy to X. Yu, FGI

Autonomous Driving

Information Systems

Decision Support Systems

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Full process chain merging virtual and

physical worlds Zhu, L., Hyyppä, J., Kukko, A., Kaartinen H., and R. Chen,

2011. Photorealistic building construction from mobile laser

scanning data. Remote Sensing 3(7), 1406-1426

Indoor MLS

Courtesy to R. Kaijaluoto, FGI

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Page 38

Forest SLAM based driver support and forest inventory system

• Forest SLAM

• Driver selects the trees to be harvested in the HMI display

• Semiautomatic control of movements of the crane and harvester head in normal easy conditions

• Driver has more time for perception and decision making

• The collected map of trees improves the analysis and planning of forest operations when uploaded to GIS

Partner: Aalto, Finnish Forest Research

Institute

Companies: Ponsse, Parker-Vansco, UPM-

Kymmene, Stora-Enso, Metsäliitto, Metsäteho

Courtesy to A. Visala, Aalto

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Mini-UAV Laser Scanning

FGI Sensei – first mini-

UAV LS (2009)

• NovAtel SPAN-CPT

• Ibeo Lux/Sick LMS151

• AVT Pike F-421C

• Specim V10H

• Flir Photon 320 2nd generation


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Jaakkola, A., Hyyppä, J., Kukko, A., Yu, X., Kaartinen, M., Lehtomäki, M., and Y. Lin, 2010, A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. ISPRS journal of Photogrammetry and Remote Sensing 65 (6), 514-522

World’s first mini-UAV Laser Scanner SENSEI

3rd gen UAV LS

• Lighter

• Higher PRF

• Better range accuracy

• Better planimetric accuracy

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Applications and Impact

• Ecological for measurements (no transfer cost/pollution of

large planes)

• Effective for Change-detection and multitemporal surveys

(data also obtained when needed)

• Simulates ALS, but also for reference data collection

• Costs are going down

• New UAV applications

• Powerline measurements

• Operational pilot in September 2014 (first in the world)

UAV LS

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Tree Extraction with SENSEI

Method n Horizontal mean Horiz. StDev Height mean Height StDev

Manual 17 40 cm 14 cm -15 cm 30 cm

Automatic 55 110 cm 56 cm 2 cm 34 cm

100 200 300 400 500 600 700

100

200

300

400

500

600

700

8000

5

10

15

20

Jaakkola, A., Hyyppä, J., Kukko, A., Yu, X., Kaartinen, M., Lehtomäki, M., and Y. Lin, 2010, A low-cost multi-sensoral mobile mapping system and its feasibility for tree measurements. ISPRS journal of Photogrammetry and Remote Sensing 65 (6), 514-522

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World’s first mini-UAV-LS operational

corridor mapping, Sept 2014


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Mobile and Personal

Laser Scanning: System

and applications Dr. Antero Kukko

Centre of Excellence in Laser Scanning Research

Finnish Geospatial Research Institute FGI

EuroSDR MLS Accuracy evaluation

ROAMER June 2009

Intensity

Mitsubishi Sep 2010

RGB

Riegl March 2010

Elevation

Sensei May 2011

Kaartinen, H., Hyyppä, J., Kukko, A., Jaakkola, A. and H. Hyyppä, 2012.

Benchmarking the Performance of Mobile Laser Scanning Systems Using

a Permanent Test Field. Sensors 12 (9), 12814-12835;

Courtesy to H. Kaartinen, FGI

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MLS Accuracy Evaluation • Elevation error vs. distance

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0

Ele

va

tio

n e

rro

r [m

]

Distance from trajectory [m]

MLS A

MLS A recalibrated

MLS B

MLS B recomputed

MLS C

MLS D

MLS E

Courtesy to H. Kaartinen, FGI

• GNSS-IMU Positioning

• Ultra High Speed Laser

Scanner

• Cross-track Profiling

• To gain:

• Mobility and accessibility

• Efficiency

• Object complexity

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PLS - Integrated Technology

For collecting 3D data on the move

(i.e. mobile mapping)

Courtesy to A. Kukko, FGI

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PLS under thick canopy

• In GNSS shadowed areas accuracy is reduced

due to the loss of signal:

• RMSE XY: 0.73 m

• STD XY: 0.82 m

• RMSE h: 1.10 m

• 8 targets,

• n=53,

• traj. length 3 km

Trajectory correction:

mean XY 15.9 cm

and h 8.1 cm

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PLS in Forestry

• For collecting complete structural data in complex environments

• Automation in tree inventories at plot and individual tree level

Liang et al., 2014

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Courtesy to A. Kukko & X. Liang, FGI

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Industrial engineering

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• As-built inventories for plant layout design

• Existing structures

• Pipelines

• Connections

• Masses and materials

• Complex scenes

• Demand for level of detail

• Demand for accuracy


Frame-based

Spectrometry Dr. Antero Kukko

Centre of Excellence in Laser Scanning Research

Finnish Geospatial Research Institute FGI

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FGI hyperspectral, photogrammetric UAV

measurement system: Summer 2014 setup

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Courtesy to E. Honkavaara, FGI

Output products: hyperspectral image

mosaics, DSMs, point clouds, spectra, BRF,

σXYZ, σρ

0

0.1

0.2

0.3

0.4

0.5

0.6

500 700 900

Re

fle

cta

nce

Wavelength (nm)

2724

2512

2028

1497

994

632

Courtesy to E. Honkavaara, FGI

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Utilizing individual

tree information in

laser assisted forest

inventory

New Sensor Technology – Future

of Remote Sensing and GIS

Documents

New possibilities of remote sensing, point clouds, laser ... · 20/11/2015 1 New possibilities of remote sensing, point clouds, laser scanning and UAV LS Juha Hyyppä Director, NLS/FGI,