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Jonathan Li, PhD, PEng, OLS/OLIP, SM-IEEE

Professor and Director, Centre for Remote Sensing & Spatial Informatics

School of Information Science and Engineering

Xiamen University, China

Professor and Director, Waterloo Lab for Geospatial Technology and

Remote Sensing (GeoSTARS Lab), Faculty of Environment

University of Waterloo, Canada

Chair, ICWG I/Va on Mobile Scanning and Imaging Systems for Surveying

and Mapping, International Society for Photogrammetry and Remote

Sensing (ISPRS), 2012-2016


• Mobile Laser Scanning: An Overview

• Our Recent Work in Use of MLS

– Road Surface Segmentation

– Road Surface Crack Detection

– Road Marking Extraction

– Road Curbstone Detection

– Roadside Tree Crown Extraction

– Extraction of 3D Street Light Poles

• Trends in Mobile Laser Scanning

• About ISPRS ICWG I/Va


GPSVan, late 1980s (Bossler et al., 1991)

VLMS, 1999 (Manandhar and

Shibasaki, 2001)

VISAT , early 1990s (Schwarz et al., 1993)


10 sq km/hr

Speed of data

capture

Terrestrial

Laser

Scanning

Mobile

Laser

Scanning

(MLS)

Airborne

Laser

Scanning

(ALS)

0.1 sq km/hr

(TLS)

€100k €600k

Capital cost

€400-500k


+ =

ALS point-cloud MLS point-cloud Full coverage


Both static TLS and ALS systems have their limitations

for the rapid and cost effective capturing of 3D data

from larger road sections.

This is especially true if these sections include

tunnels or if dense point coverage of the building

facades is required.

Laser scanner(s) onboard a mobile platform, a MLS

system should be used.


• MLS suppliers: Optech, Riegl, Trimble, Topcon, MDL or

SITECO.

• StreetMapper (2005), 3D Laser Mapping

• LYNX Mobile Mapper (2007), Optech

• VMX-250 (2009), Riegl

• Road-Scanner (2009), SITECO

• IP-S2 (2009), Topcon

• MX8 (2010), Trimble

• Dynascan (2010), MDL Laser Systems

• StreetMapper 360 (2011), 3D Laser Mapping

• VMX-450 (2011), Riegl


OPTECH Lynx Mobile Mapper,

2011 (Li et al., 2011)

RIEGL VMX-450, 2013

(Li et al., 2013)

Trimble MX-8, 2012

(Li et al., 2012)


• Positioning Sensors (GPS, IMU and DMI) are vehicle-oriented; to

determine absolute locations of the MLS platform with respect to

a global coordinate system (WGS-84).

• Mapping Sensors (laser scanners, video or digital cameras, etc.)

are feature-oriented; to provide the positional information and

attributes of objects (features) relative to the vehicle in a local

coordinate system.

MLS System

Architecture


Time-of-flight (TOF) scanners (e.g., Riegl,

Optech) send a short laser pulse to the

target; the time that elapses between

emitted pulse and received pulse

correlates to the range.

Phase shift scanners (e.g., Faro, Z+F)infer

the range by measuring the phase

difference between the emitted and

received backscattered signal of an

amplitude modulated continuous wave

(AM CW).

Better accuracy, but shorter ranging.

Riegl VQ-450 laser scanners

mounted on VMX-450 system

Faro PHOTON 120 mounted on

ROAD-SCANNER system


• The use of multiple camera arrays to provide

360º panoramic images in the horizontal plane

is very common (e.g., VMX-450).

• Fully integrated multiple cameras have also been

used (e.g. LADYBUG unit used in IP-S2).

LADYBUG with 6

SONY CCD

cameras.

CS6 Camera System supporting

up to 6 digital colour cameras


System Road Scanner Topcon

IP-S2

Trimble

MX8

Street

Mapper

Riegl

VMX-450

Optech

Lynx

Scanner

Max. range

Range

precision

Range

accuracy

Faro

Photon 120

120m

(ρ90%)

1mm

@ 25m, ρ90%

± 2mm

@25m

Sick

LMS 291

80m

(ρ10%)

10 mm

@ 20 m

VQ-250 VQ-450 V200

500m(ρ80%)

75m (ρ10%)

5mm

@150m (1σ)

± 35mm ± 10mm

@150m (1σ)

800m(ρ80%)

140m(ρ10%)

5mm(1σ )

200m

(ρ80%)

8mm

(1σ )

± 8mm(1σ) ± 10mm

(1σ)

PRR 122- 976 kHz 40kHz 2 x 300 kHz 2x 550kHz 2 x 200 kHz

Scan speed 48Hz 75Hz 2x 100 Hz 2x 200 Hz 2x 200 Hz

Scanner

FOV

H360º / V320º 180º / 90º 360º

without gaps

360º 360º

Angular . 1º / 0.5º 0.001º 0.001º 0.001º


Prof. Cheng Wang

Dr. Haiyan Guan

Yongtao Yu

PhD Student

Yangbin Lin

PhD Student


• Compared with advances in MLS systems, automated algorithms and

software tools for efficient information extraction from MLS point

clouds rather fall behind, due to huge data volumes (e.g., 660 million

points for a 10 km road section at a speed of 60 km/hr) and

complexity of urban street scenes.

• To extract road-surface and roadside objects, the MLS point clouds

need first to be classified into different categories (e.g., road, building,

tree), which is a key step for accurate identification and 2D/3D

reconstruction of such objects.

• Different from ALS data processing, we have to deal with fully 3D

point clouds. Due to the non-unique correspondence between (X, Y)

coordinates and Z coordinate, existing filtering and classification

algorithms have difficulties to handle huge amounts of MLS data.


• Mobile Laser Scanning: An Overview

• Our Recent Work in Use of MLS

– Road Surface Segmentation

– Road Surface Crack Detection

– Road Marking Extraction

– Road Curbstone Detection

– Roadside Tree Crown Extraction

– Extraction of 3D Street Light Poles

• Trends in Mobile Laser Scanning

• About ISPRS ICWG I/Va


Road Surface Segmentation

(Curb Stone Lines)

Road Crack Detection

MLS Point Clouds

Road Curb-stone Extraction

Road Marking Extraction

Street Light Pole Extraction

Tree Crown Extraction


Raw MLS Points

Generating Profiles on Trajectory

Profile Gridding

Elevation Gradient Computation

Curb-stone Point Detection

Road Surface Segmentation


Rg=3 m

Sg=20 cm


Curbs 1,2

Curbs 3,4


• Mobile Laser Scanning: An Overview

• Our Recent Work in Use of MLS

– Road Surface Segmentation

– Road Surface Crack Detection

– Road Marking Extraction

– Road Curbstone Detection

– Roadside Tree Crown Extraction

– Extraction of 3D Street Light Poles

• Trends in Mobile Laser Scanning

• About ISPRS ICWG I/Va


Road Point Cloud

Generation of

Georeferenced Feature Image (GFI)

Binary Thresholding

Tensor Voting for Curvilinear Feature

Detection

Morphological Thinning for Crack

Extraction


Thresholded GFI

(1- cracks, 0- non-cracks)

Curvilinear feature

detection from noisy

thresholded GFI using a

tensor voting approach

Crack extraction by

morphological thinning


• Mobile Laser Scanning: An Overview

• Our Recent Work in Use of MLS

– Road Surface Segmentation

– Road Surface Crack Detection

– Road Marking Extraction

– Road Curbstone Detection

– Roadside Tree Crown Extraction

– Extraction of 3D Street Light Poles

• Trends in Mobile Laser Scanning

• About ISPRS ICWG I/Va


Thresholded GFI

(1 for road markings

0 for non-road markings)

Area-feature detection

from noisy thresholded

GFI using a tensor voting

approach

Road marking extraction

by morphological

thinning


• Mobile Laser Scanning: An Overview

• Our Recent Work in Use of MLS

– Road Surface Segmentation

– Road Surface Crack Detection

– Road Marking Extraction

– Road Curb-stone Detection

– Roadside Tree Crown Extraction

– Extraction of 3D Street Light Poles

• Trends in Mobile Laser Scanning

• About ISPRS ICWG I/Va


Raw coloured point cloud of a road


Point cloud of Road using the Ambient Occlusion approach


Less than 5 seconds to detect curb-stone candidates from 500Mb point-cloud


• Mobile Laser Scanning: An Overview

• Our Recent Work in Use of MLS

– Road Surface Segmentation

– Road Surface Crack Detection

– Road Marking Extraction

– Road Curbstone Detection

– Roadside Tree Crown Extraction

– Extraction of 3D Street Light Poles

• Trends in Mobile Laser Scanning

• About ISPRS ICWG I/Va


Non-road Point Cloud

Generation of Elevation-based GFI

Generation of Seed-Points of Tree

Crowns

Tree Crown Extraction Using Marked

Point Process


• Select the pixels with local maxima from the

elevation-based GFI within a pre-defined circular

neighbourhood as the seed-points.

• Group all the seed-points belonging to the same tree

crown and generate a single candidate point by

centralizing.


Red - before transformation

Yellow - after transformation


• Mobile Laser Scanning: An Overview

• Our Recent Work in Use of MLS

– Road Surface Segmentation

– Road Surface Crack Detection

– Road Marking Extraction

– Road Curbstone Detection

– Roadside Tree Crown Extraction

– Extraction of 3D Street Light Poles

• Trends in Mobile Laser Scanning

• About ISPRS ICWG I/Va


Raw point cloud

Non-road points

Oliver van Kaick et al., 2013. Bilateral Maps for Partial Matching, SIGGRAPH.


• Mobile Laser Scanning: An Overview

• Our Recent Work in Use of MLS

– Road Surface Segmentation

– Road Surface Crack Detection

– Road Marking Extraction

– Road Curbstone Detection

– Roadside Tree Crown Extraction

– Extraction of 3D Street Light Poles

• Trends in Mobile Laser Scanning

• About ISPRS ICWG I/Va


• ROAMER sensor equipment

FARO Photon 120 scanner

• 122-976 000 pts/s, user selectable, typically 244 or 488 kHz

• 320 ° maximum field of view

• 3-61 Hz scan frequency , user selectable, typically 48 or 61 Hz

• 785 nm wave length

NovAtel SPAN GPS-IMU

• NovAtel DL-4plus receiver and GPS-702 antenna,

• L1 and L2 frequencies

• Honeywell HG1700 AG11 tactical-grade RLG IMU

• Gyro bias 1.0 deg/h

• Random walk 0.125 deg/rt-hr

• Data rate 100 Hz

Bi-trigger synchronization

• In-house built electronics

• Scanning start-stop

• Delivers scanner triggers to receiver log

Kukko et al. 2012. Multiplatform Mobile Laser Scanning: Usability

and Performance, Sensors. 12(9): 11712–11733.

52


• Indoor - Mobile Mapping System (iMMS) from

Viametris, France

• Real-time location using Simultaneous

Localization And Mapping (SLAM) algorithm

• 3D reconstruction through 2 additional LiDAR

• Panoramic camera: Ladybug 3

• Range: 0.1 m – 30 m

• No INS, No GNSS required

• No drift, localization at 1 cm level

• Scanning speed at 86400 points per second


• MIT's real-time indoor mapping

system uses Kinect, laser to aid rescue

workers

• It consists of a Microsoft Kinect RGB-D

sensor, Microstrain 3DM-GX3-25 IMU,

and Hokuyo UTM-30LX LiDAR. The

electronics backpack includes a laptop

and a barometric pressure sensor.

• Scans a building in a 270-deg arc

• Multi-floor mapping in real time.


• Hand-held device developed in Australia

• Scanning at walking speed

• The scanner goes where you go

• Long lasting

• No GNSS required

• Lightweight 700g

M. Bosse, R. Zlot, P. Flick, Zebedee:

Design of a Spring-Mounted 3-D

Range Sensor with Application to

Mobile Mapping, IEEE Transactions

on Robotics, 28(5), Oct 2012


• Mobile Laser Scanning: An Overview

• Our Recent Work in Use of MLS

– Road Surface Segmentation

– Road Surface Crack Detection

– Road Marking Extraction

– Road Curbstone Detection

– Roadside Tree Crown Extraction

– Extraction of 3D Street Light Poles

• Trends in Mobile Laser Scanning

• About ISPRS ICWG I/Va


• ISPRS Inter-Commission Working Group I/Vs on Mobile Scanning

and Imaging Systems for Surveying and Mapping (2012-2016)

Jonathan Li

Chair

Antonio Tommaselli

Co-Chair

Kai-Wei Chiang

Co-Chair

Alberto Guarnieri

Secretary


Contact:

Prof. Dr. Jonathan Li

Faculty of Environment,

University of Waterloo

200 University Avenue, Waterloo, ON

Canada N2L 3G1

junli@uwaterloo.ca

School of Information Science and

Engineering, Xiamen University

422 Siming Road South, Xiamen

China 361005

junli@xmu.edu.cn

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