measurement technologies and modelling...

ALL TERRAIN SCANNING AND MODELLING

MEASUREMENT TECHNOLOGIES AND MODELLING METHODS

Gian Matteo Bianchi BSc MSc

Ph.D. student @ University of Rome - Tor Vergata

Principal Engineer Road Load Data @ Jaguar Land Rover

22 Nov 2018

Outline

The way to road scanning

2

• Pavement Surface Characteristics: PIARC categories and common road KPIs

• Measurement technologies

• Surface modelling: from point clouds to usable models, the CRG format

• A new approach to surface scanning: “an autonomous road scanning system”

The 4 Pillars of a CAE Simulation

Closing the loop between physical and virtual test

3

PAVEMENT SURFACE CHARACTERISTICS

INFLUENCE ON VEHICLE ATTRIBUTES

Texture Wavelength

An important concept

5

P. Pereira - Skid resistance and texture of compacted asphalt

mixes evaluated from the ifi in laboratory preparationISO13473-2:2002

λ

Pavement Surface Characteristics

Geometrical properties

6

1µm 10µm 100µm 1mm 10mm 100mm 1m 10m 100m

Microtexture Macrotexture Megatexture Roughness

Ride Quality

Wet Weather Friction

Dry Weather Friction

Vehicle Wear

In-Vehicle Noise

Texture

Wavelength

PIARC Category

Pavement

Surface

Characteristic

Influence

Tyre Wear

Rolling Resistance

Derived from XVIIIth World Road Congress in Brussel (Sept. 1987)


Is it only civil engineering?

7

VTI - History of Road Profile Measurements

and Current Standards in Road Surface Characteristics

Beginning of XXth century

Highway Research Board bulletin -

Devices for recording and evaluating pavement roughness.

First mobile profilers – General Motors (1969)

TSL s.r.l. – High speed road profiler for macro-texture measurements (2013)


A measure of comfort – International Roughness Index (IRI)

8

• The IRI is used worldwide by many

authorities

• An indicator of the pavement condition

• Measured using the road profile from 1.2 m

to 30 m (wavelength)

• Data is processed using a quarter-car “filter”

which represents the response of a standard

vehicle

• High IRI numbers mean that the road has

more imperfections, erosions,

depressions…worse ride quality!The Little book of Profiling – University of Michigan (1998)


A measure of safety – International Friction Index (IFI)

9

• It’s a wet friction estimator

• The IFI quality is still debated

• Derived from internationally recognised

measurements: skid resistance and macro

texture (generally in the form of MPD)

𝐹 𝑆′ = 𝐹60𝑒(𝑆′−60)/𝑆𝑝

Function of

Macrotexture

Function of

both Macrotexture &

Skid Resistance


A measure of safety – International Friction Index (IFI)

10

• The Mean Profile Depth: it’s an estimate of

the surface’s capability to drain water,

reducing risks of aquaplaning, spray and

splashes. ISO13473-1(2004)

• The Skid Resistance is directly measured

using two principles BS7941:

− Partially locked wheel, 15% slip ratio

− “Fix the vertical plan of the wheel at

20% to the line of chassis” (slip angle)

PK1PK2

𝑀𝑃𝐷 =𝑃𝐾1 + 𝑃𝐾2

2−𝑀𝑒𝑎𝑛

Mean

100 mm

Am

plit

ude [m

m]

Distance [mm]


Skid resistance – measurement example

11

Std. Tarmac Filler Tarmac

Polished Tarmac

ParameterStd.

Tarmac

Filler

Tarmac

MPD [mm] 1.03 1.35

RMS [mm] 0.788 1.017

Standard Tarmac

Mean 0.75

Filler Tarmac

0.9

Polished Tarmac

0.8

𝑅𝑚𝑠 =1

𝑙 0

𝑙

𝑍2 𝑥 𝑑𝑥


A relationship with efficiency – Rolling Resistance

12

Rolling resistance is mainly due to the (low frequency) hysteretic energy loss at the tyre-

road interface while the tyre deflects and flattens over the road surface

• Low rolling resistance tyres can reduce fuel consumptions by 1-2%

• It accounts for up to 30% of the usable energy* (based on speed)

• It is not only a property of the tyre but it’s due to the interaction between tyre and

surface

• The influence of this interaction is still debated, however independent studies have

proved that MPD and IRI can contribute to the RR up to 50%

*NHSTA Tire Fuel Efficiency Consumer Information Program Development Phase 2

MEASUREMENT TECHNOLOGIES FOR ROAD SCANNING

A BRIEF OVERVIEW

Light Detection and Ranging (LiDAR)

Back to basics

14

LASER emitter

Photodetector

𝜏 =2 ∗ 50 𝑚

3 ∗ 108 𝑚/𝑠= 300 𝑛𝑠

∆𝑑 = 10 𝑚𝑚 → ~0.07 𝑛𝑠

Time of Flight (ToF)

𝑑

Time

Counter

𝑑 =𝑐 ∆𝜏

2Resolution issue

∆𝜏

Am

plit

ud

e

Time


Back to basics

15

LASER emitter

Photodetector

Phase Shift

∆𝜑

𝑑𝑟𝑎𝑛𝑔𝑒1 =𝑐

2𝑓1=

𝑐

2 ∗ 2 𝑀𝐻𝑧= ~76 m

Ambiguity range based on modulation frequency

𝑑𝑟𝑎𝑛𝑔𝑒2 =𝑐

2𝑓2=

𝑐

2 ∗ 125 𝑀𝐻𝑧= ~1.2 m

𝑑 =∆𝜑 ∗ 𝑐

4 𝜋 𝑓

Phase detector

Am

plit

ud

e

Time


Market availability

16

What’s important?

− Angular Resolution --> Cartesian resolution

− RR and Accuracy --> quality

− Rotational Speed --> time!

− Degrees of Freedom --> 1 or 2, multibeam


Horizontal plan resolution

17

• Angular resolution is critical and it

follows trigonometry rules e.g.:

• 0.0072 deg equals ~25 mm point-to-

point resolution @ 20 m

• 0.0036 deg equals ~12 mm point-to-

point resolution @ 20 m

• Doubling the angular resolution

increases the acquisition time by a

factor of 4 (on 2 axis LiDAR)


Point density

18

• Angular resolution and sampling

distance give the point cloud density

• 2000 points / m2 is equivalent to a

point-to-point resolution of ~ 22 mm

• Reversing the math, a 5x5 mm grid

requires 40’000 points / m2

𝐷𝑒𝑛𝑠𝑖𝑡𝑦 =1

𝑅𝑒𝑠2

In the above example:Scanning area = 40 x 12 m2

Average density = 1460 points / m2

Average resolution = 26 x 26 mm

Total Number of points = 70 Million


Potential scenarios

19


Integration and application

20

Terrestrial

Laser Scanning (TLS)

Mobile

Laser Scanning (MLS)

Airborne

Laser Scanning (ALS)

Horizontal Resolution: 1 x 1 mm

Vertical Accuracy: 1 - 20 mm

Equipment Cost: Medium to High

Running Cost: High

Acquisition time: High



Equipment Cost: High

Running Cost: Medium

Acquisition time: Medium to Low




Running Cost: Low



Integration and application

21

Terrestrial

Laser Scanning (TLS)

Mobile

Laser Scanning (MLS)

Airborne

Laser Scanning (ALS)



Equipment Cost: Medium to High

Running Cost: High

Acquisition time: High




Running Cost: Medium





Running Cost: Low



Inertial reference – MLS and ALS

22

𝑥𝑝𝑦𝑝𝑧𝑝

= 𝑥 = 𝑥𝐼𝑀𝑈(𝒕) + 𝑀𝐼𝑀𝑈(𝒕)(𝑀𝐿𝑆𝑡𝑜𝐵 𝑟𝐿𝑆(𝒕) + 𝑟𝐿𝐴)

target Frame IMU position

in target frame

IMU orientation

in target frame

Sensor to body

frame orientation

Measured point

in sensor frame

Lever Arm

• GNSS + IMU accuracy can be as little

as 10 mm on x and y

• Z accuracy is limited by the angle at

which the receiver can see the

satellites, around 10-60 mm

• Body Roll, Pitch and Yaw are

important as well as position

• Boresight calibration is another source

of “static” errors


Can we see everything?

23



Ride Quality



Vehicle Wear

In-Vehicle Noise

Texture

Wavelength

PIARC Category

Pavement

Surface

Characteristic

Influence

Tyre Wear

Rolling Resistance

Airborne Laser Scanning

Mobile Laser Scanning

Terrestrial Laser Scanning

Texture Sensors

Back to basics

24

LASER emitter

Laser Triangulation

𝛼

baseline

Stereoscopy

baseline

Photogrammetry

𝑋𝐿 𝑋𝑅

Other technologies are available mainly for lab activities: induction, AFM, stylus, interferometer….

Texture Sensors

Laser triangulation

25

• Based on simple math and physics

• High speed > 60 KHz

• High RR and Accuracy but relative to the

measurement range

• Vertical resolution ~1 um

• Horizontal resolution limited by laser spot

size ~20-50 um

• It can be fitted on a motorised stage or on

a running vehicle….

• Target material optical properties may

create issues (specular or diffusive)!

• 2D variants can be adopted as well

ℎ =𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒

tan 𝛼

𝛼

baseline

ℎ

Texture Sensors

Image correlation - principle

26

baseline

𝑋𝐿 𝑋𝑅

𝑧 =𝑓 ∗ 𝑏𝑎𝑠𝑒𝑙𝑖𝑛𝑒

𝑋𝐿 − 𝑋𝑅

𝑋𝐿 = ~1500 𝑋𝑅 = ~1750

Disparity

System setup

& calibration

𝐶1 𝐶2

Texture Sensors

Image correlation – disparity estimation

27

𝑋𝐿 = ~1500 𝑋𝑅 = ~1750

𝜌 = 0

“Block” Cross Correlation

𝜌 𝑑𝑖𝑠𝑝𝑎𝑟𝑖𝑡𝑦 =1

𝐾

𝑑𝑖𝑠𝑝𝑎𝑟𝑖𝑡𝑦=0

max _𝑑𝑖𝑠𝑝

𝐶1 𝑋𝐿 𝐶2 𝑋𝐿 + 𝑑𝑖𝑠𝑝𝑎𝑟𝑖𝑡𝑦

XR = XL − max( 𝜌 𝑑𝑖𝑠𝑝𝑎𝑟𝑖𝑡𝑦 )

𝜌 = 0.25

𝜌 = 1

𝜌 = 0.75

Very simplified approach, it’s a 2D problem!

Texture Sensors

Image correlation - limits

28

• Depth estimation from digital images

• Lots of math and estimations

• Quality (RR, resolution and accuracy)

depends on many factors: lens, camera

sensor, distance from object, setup…

• Horizontal resolution ~20-200 um

• Exposure is critical….

• Light source needs to be homogeneous

and controlled or results may be deeply

affected!

Balanced exposure level

Over-exposed

Texture Sensors

Image correlation

29

• Depth estimation from digital images

• Lots of math and estimations

• Quality (RR, resolution and accuracy)

depends on many factors: lens, camera

sensor, distance from object, setup…

• Horizontal resolution ~20-200 um

• Exposure is critical….

• Light source needs to be homogeneous

and controlled or results may be deeply

affected!

SURFACE MODELLING

FROM POINT CLOUDS TO USABLE MODELS, THE CRG FORMAT

Damper Response at Full Bump

As measured during a Road Load Data collection

31

Dam

per

Body F

orc

e [

kN

]

Vertical Suspension Travel [mm]

2.5 kN / mm

8.5 kN / mm

KDO 150 mm

Surface Modelling

Process workflow

32

Data Collection

(survey)

PCs

Registration

Complete PC

Cleaning

Meshing and

Processing

Usable Terrain

Model (CRG)

Geographical

Coordinates

and Metadata

CRG Library

Developed in-house

PC – Point Cloud

Surface Modelling

What is a point cloud (PC)?

33

It’s a collection of 3D scattered points

X = 1.251

Y = 0.215

Z = 0.535 m

Intensity = 100

Surface Modelling

Data collection (survey) – laser scanning

34

• Planning is crucial to get the right data

(resolution, missing info…)

• TLS requires a scan every ~5-20 m, time

consuming (based on resolution and surface

morphology)

• LS targets support processing and can be

used to import geo-graphical coordinates

• A LiDAR can see what you see,

line of sight is required

• Moreover, optical technologies don’t cope very

well with water/rain (scattering!)

Surface Modelling

Data collection (survey) – geographical information

35

• Geographical references can be imported in

the PC for post-processing

• Targets needs to be surveyed using

appropriate equipment called GNSS Rover

• Horizontal GNSS RTK accuracy is ~10mm

• Vertical GNSS RTK accuracy is ~20 mm

Lat. 66.0967221… deg

Long. 17.9830412… deg

Temp. -26 ºC

Surface Modelling

Point cloud registration

36

• It is necessary to build a unique PC starting

from different scan locations

• Each PC/scan location needs to be centred

and oriented to match one with the other

• 6 DoF (space and orientation)

• It’s power and time consuming

• This process can get to mean PC-to-PC

registration accuracies down to 0.8 mm

• The process is “similar” to the image cross-

correlation techniques and it can be facilitated

by physical targets

Surface Modelling

Point cloud registration

37

Surface Modelling

Point cloud cleaning

38

• It’s a manual process

• It takes time, based on the size and complexity

of the scenario

• It involves removing all undesired debris

• A leaf in a PC will be seen a “stone” in a CAE

simulation (surface deformation won’t be

modelled)

4 hours later….

Surface Modelling

Curved Regular Grid (CRG) format - OpenCRG

39

• It was created by Daimler AG (Germany)

• Highly efficient way to store the road geometry

• No need to store 3 coordinates for each point, like in

a Point Cloud (less data, faster processing)

• The surface trajectory is defined by the reference

line x(u), y(u), phi(u)

• The elevation z(u,v) is defined as a regular grid in

respect to the reference line position

• Data is stored in an un-curved regular grid

− u is the travelling direction

− v is transverse to the travelling direction

− z is queried by a unique u and v

− phi is the heading of the reference line

From OpenCRG User Manual

phi(u)

Surface Modelling

Curved Regular Grid (CRG) format - OpenCRG

40

From OpenCRG User Manual

• A constant spacing causes grid deformation on

corners, based on corner radius

• CRG can contain only static data in the Z matrix (e.g.

elevation, friction coefficient, temperature, grey

intensity….)

• The CRG format can’t be used to model any dynamic

behaviour in the tyre-surface interaction (e.g. speed

dependant friction levels)

• The CRG can store extra information \ metadata (e.g.

GPS coordinates, scaling factors, comments….)

• It’s open source!

Surface Modelling

The triangulation of a scattered point (cloud) set

41

• Many algorithms can be used to interpolate data over a

desired (regular) grid: triangulation, inverse distance,

radial, natural neighbour.....

• Delaunay triangulation is simple and effective

• It produces the largest minimal angle for each triangle

• The circle circumscribing any triangle contains no other

points of the original scattered PC

• It is commonly used in Finite Element Method (FEM)

• A generic point can be then extracted using interpolation

methods (linear, cubic….)

P1

P2

P3

P4

𝑧 = 𝑎𝑥 + 𝑏𝑦 + 𝑐

By 3 points there is one plane

Any point within the triangle

can be interpolated

Surface Modelling

Gridding\Meshing workflow

42

1. The triangulation is calculated

(heavy processing on a large dataset)

2. A regular grid is created, matching the required CRG

width and length

3. Each point (xn,yn) on the regular grid is interpolated on

the triangulation, estimating the zn

• Only points within the convex hull can be interpolated,

care needs to be provided while surveying\scanning

• Grid slicing and parallel processing can make this

process 10 times faster

• Time and computational complexity of meshing requires

𝑂(𝑟𝑒𝑠2) where 𝑟𝑒𝑠 is the regular grid resolution

Surface Modelling

From PC to CRG – visual example

43

Mesh size 10 mmOriginal PC

A NEW APPROACH TO SURFACE SCANNING

AN AUTONOMOUS ROAD SCANNING SYSTEM

An Autonomous Road Scanning System

Overview

45

• Measurements are taken in steady condition

(no inertial reference required)

• From 100 m down to 30 um

• Position and orientation is logged only to support post-

processing (geographical information)

• Path follower, autonomous driving based on a known

plan

• Max speed 1 m/s (currently)

• Same acquisition time as TLS but extra information is

acquired (micro-texture and track temperature)

• Patent Issued on Feb 2018


System integration

46

• Developed in Robotic Operating System (ROS)

• Wireless capabilities up to 1 km (LoS)

• Safety features include: watchdogs, emergency braking

and remote kill switch

• Comms: Wi-Fi, eth, CAN, serial

• Ground station developed in-hose


Results

47

• Given a plan (track or grid), the robotic platform follows

the path, stopping at each measurement point while

driving along waypoints

• User sets spacing between TLS, texture and

temperature measurements

• The ground station provides a feedback on system

status and control parameters

• Suitable for proving grounds and confined test areas

Track Temperature = 18 °C


Toward friction estimation

48



Ride Quality



Vehicle Wear

In-Vehicle Noise

Texture

Wavelength

PIARC Category

Pavement

Surface

Characteristic

Influence

Tyre Wear

Rolling Resistance

Airborne Laser Scanning

Mobile Laser Scanning

Terrestrial Laser Scanning

JLR – Autonomous Road Scanning System

Almost 2 orders of magnitude

Jaguar Land RoverW/1/26 Abbey Road, WhitleyCoventry CV3 4LF, UK

jaguarlandrover.com

THANK YOUGrazie!

Gian Matteo Bianchi BSc MSc PhD StudentPrincipal Engineer Road Load Data

M +44(0)7469 416805

[email protected]

49

University of Rome – Tor VergataVia del Politecnico 100133 Rome, Italy

ing.uniroma2.it

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