Recent developments in laser scanning

Kourosh Khoshelham

With contributions from:

Sander Oude Elberink, Guorui Li, Xinwei Fang, Sudan Xu and Lucia Diaz Vilarino

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Why laser scanning?

Laser scanning accurate capturing of 3D geometry Point cloud;

Point clouds suitable for automated processing;

Automated processing = Fast, inexpensive, less labor intensive.

Terrestrial Laser Scanning (TLS) Mobile Laser Scanning (MLS) Aerial Laser Scanning (ALS)

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Overview of developments in laser scanning

(by no means exhaustive)

Platforms

Developments

ALS TLS MLS

Hardware

technology

Higher pulse/scan

frequency

Multiple pulses in the air

(MPiA)

Full-waveform recording

UAV-based laser scanning

Smaller, more user-

friendly scanners

Combined phase/pulse

range measurement

Full waveform recording

SLAM systems (mobile

mapping in GPS-denied

environments)

Processing

methodology

Intensity calibration

Waveform processing

Data integration and fusion

with other sources

Automated feature/object

extraction

Cloud computing

Waveform processing

Data integration and

fusion with other

sources

Automated

feature/object extraction

Cloud computing

Data integration and

fusion with other

sources

Automated

feature/object extraction

Cloud computing

Applications

3D change detection

Damage mapping

Coastal monitoring

Water management

Indoor mapping

Non-topographic

applications: Geology,

Hydrology, …

Road furniture inventory

Railway asset

management

Indoor mapping

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Developments in hardware technology:

Multiple Pulses in the Air (MPiA)

Original one pulse in the air:

Reduces pulse rate at high altitudes to avoid range ambiguity;

Multiple pulses in the air (MPiA) technology:

Allows higher pulse rates at high altitudes

From Leica ALS60

Product Specifications

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Developments in hardware technology:

Multiple Pulses in the Air (MPiA)

Year of introduction 2011 2010 2012 2010

Multiple pulses in air Yes Yes Yes Yes

Data from: http://www.geo-matching.com/

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Full-waveform recording

Year of introduction 2011 2010 2012 2010

Full-waveform digitization Yes Yes Yes Yes

Year of introduction 2010 2010 2010 2010

Full-waveform digitization No Yes No No

ALS

TLS

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More versatile terrestrial laser scanners

Year of introduction 2010 2010

Total weight (kg) 11.8 5.0

Ranging principle Phase+Pulse Phase (hypermodulation)

Max range (m) 80 120

Camera option Yes Yes

User interface Tablet PC, Tablet, Smartphone

Smaller and lighter scanners;

Better accuracy at longer range using wavepulse technology (hypermodulation);

More user-friendly interface (smartphone);

Camera option.

8

Developments in SLAM

Mobile mapping in GPS-denied (indoor) environments;

IMU errors corrected by other means (odometer, scene

structure, loop closure).

Zebedee

TMMS

From: http://www.lidarnews.com/

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Honorable mention: Kinect

RGB-D cameras like the Kinect have great potential for indoor mapping;

Kinect captures:

depth + color images @ ~30 fps

= sequence of colored point clouds

But: lower accuracy and lower depth resolution compared to laser scanning

+

IR emitter RGB camera IR camera

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Developments in processing methodology and applications:

Rail track detection and modelling

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Detection based on rail properties:

Slightly above ground;

Contact wires above at a certain height;

Locally linear;

Modeling by:

Fitting small rail pieces;

Parameter estimation by MCMC (robust to gaps and outliers);

Interpolation by smooth Fourier curves.

Rail track detection and modelling

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Developments in processing methodology and applications:

Rail track detection and modelling

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Point-model distances:

Accuracy ~2 cm, good enough for

visualisation, asset inventories;

Not for detection of rail wear

Rail track detection and modelling

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Supervised classification of connected components

Accuracy: ~ 85%

Road furniture inventory

Tree

Lamp post

Car

Facade

Other

Work of Guorui Li

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Detection of moving objects by comparing two sensor datasets

Accuracy: ~ 90%

Road furniture inventory

Work of Xinwei Fang

Sensor 1

Sensor 2

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3D change detection

Comparing point clouds from 2008, 2010 and 2012;

Based on analyzing distances between closest points. Work of Sudan Xu

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Damage mapping

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Smart cities: sensor networks coupled with 3D models

Model from Google 3D warehouse

1st floor 2nd floor Image from ESRI City Engine

3D city model

Indoor model

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(Semi-) automated modelling of indoor environments

Oriented point cloud

Work of Lucia Diaz Vilarino

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Segmented point cloud

(Semi-) automated modelling of indoor environments

Work of Lucia Diaz Vilarino

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Intersecting adjacent planes vertices

(Semi-) automated modelling of indoor environments

Work of Lucia Diaz Vilarino

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Model with faces and vertices

(Semi-) automated modelling of indoor environments

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Textured model

(Semi-) automated modelling of indoor environments

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Summary, future trends and challenges

Laser scanning already an established technology;

But still developing at a steady pace: Laser scanner hardware technology (MPiA, full-waveform, …);

Processing methodology (automated information extraction);

Applications (roads, railroads, indoor environments, …).

Future trends: Indoor mapping systems, SLAM;

Big Data, not only handling but learning from it more automation in

information extraction;

Cloud computing;

…?

Challenges in practice: …?