OPTICAL NAVIGATION SENSOR

Incorporating Vehicle Dynamics Information in Mapmaking

Tibor Takács and Viktor Kálmán

Department of Control Engineering and Information Technology, Budapest University of Technology and Economics

Magyar Tudósok körútja 2.,Budapest, Hungary

Keywords: Mobile robot navigation sensor, vehicle dynamics, map making.

Abstract: Accurate odometry and navigation may well be the most important tasks of a mobile robot’s control system.

To solve this task it is necessary to utilize proper sensors which provide reliable information about the

motion. This paper presents the prototype of an optical navigation sensor which can be an alternative choice

for the dead reckoning navigation system of a vehicle or mobile robot. The last part of the paper presents

another application in an inertial navigation system that enables a new approach to map making which

incorporates vehicle dynamics into the world map.

1 INTRODUCTION

Autonomous mobile robots gain more and more

importance in automotive, domestic and military

applications. Sensing and interpreting their

surroundings correctly are crucial in all these areas.

The sensor presented in this paper aims to improve

existing odometry methods, and to aid the creation

of world maps that incorporate the state of the

vehicle in the world map and classifies terrain

accordingly. In the first half of this document an

overview of the principle and the prototype of the

optical navigation sensor are presented, the second

section of the paper proposes a method for world

model building for mobile robots and shows some

other application where this sensor can be used.

2 NAVIGATION TECHNIQUES

Accurate self localization is one of the most

important topics in autonomous vehicle technology.

During centuries researchers have developed a lot of

techniques to measure the precise position of land or

aerial vehicles and mobile robots.

Global methods (e.g. GPS) determine directly the

solute position, but unfortunately in several cases

these are not useable, due to reception problems.

Dead reckoning methods estimate the present

ocation of a navigating agent by advancing some

previous position using known course, velocity and

time information (Borenstein, 1996).

The odometry (the most simplistic and prevalent

mplementation of dead reckoning) estimates the

course and distance of the moving agent’s path to

calculate the global location by measuring the wheel

rotation and/or the steering orientation (Dixon,

1997). This position estimating can be strongly

inaccurate under real conditions through the

mobility configuration (e.g. tracked vehicles) or

through the wheel-slippage, overacceleration or

driving over uneven floor.

3 OPTICAL NAVIGATION SENSOR

3.1 Optical Flow

Through of the inaccuracy of the odometry it is

necessary to develop a cheap sensor-system that

provides well-authenticated dislocation-data for

dead reckoning navigation in real world conditions.

The optical navigation sensor is a possible solution

to fulfil these conditions.

The working principle of the sensor is optical

flow,

namely the motion information is generated

from visual information (from an image-sequence

provided by a camera facing the ground). Typically

the motion is represented as vectors originating or

271

Takács T. and Kálmán V. (2007).

OPTICAL NAVIGATION SENSOR - Incorporating Vehicle Dynamics Information in Mapmaking.

In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, pages 271-274

DOI: 10.5220/0001648102710274

 SciTePress

terminating at pixels in a digital image sequence

(CVRG, 2000). An example is shown in figure 1.

Figure 1: The principle of the optical flow.

3.2 The Prototype of the Sensor

As a basis for further research, we have built the

prototype of the optical navigation sensor (see figure

2 and figure 3).

As a camera (what provides the image-sequence

for calculating the dislocation) we have chosen the

sensing and processing chip of a low-cost and wide-

spread optical mouse. (Hereinafter this chip is

referred to as “mouse-chip”.) The chip includes a

low-resolution camera and a DSP for calculating the

optical flow by hardware at a very high rate.

Although its resolution is relatively low, for motion

detection and measurement it is sufficient because

only the texture is important. It measures the relative

dislocations in the x and y direction.

Figure 2: The model of the sensor.

Because of the uneven floor the distance between

the sensor and the floor is continuously changing. To

compensate this effect the sensor has telecentric

optics, which has a constant magnification rate

therefore in a certain range the measurements are

independent from the height of the sensor relative to

the ground.

Naturally, it was necessary to design a

microcontroller based architecture round the mouse-

chip which offers a useable interface for a dead

reckoning navigation system. The microcontroller

reads the motion or the image information of the

mouse-chip and sends them to the processing unit,

for example to a PC in the development stage or to a

navigation or control unit at real conditions (Takács,

2007).

Figure 3: The prototype of the sensor.

3.3 Testing, Error Measurement

To measure the errors of the sensor we mounted it

on a test platform (figure 3/a) and performed some

experiments.

The first group of tests analyzes the dependence

from the texture of the floor and from the height

over ground. The experience showed that the sensor

is really insensitive to the height (thanks to the

telecentric optics) but unfortunately it sometimes

misses on shining or totally homogeneous surfaces.

This property is inherited from the mouse-chip of

the cheap optical mice. Laser equipped optical mice

might be better from this aspect. Therefore using the

mouse-chip of a laser mouse presumably solves this

problem.

With the second type of the tests we measured

the accuracy of the sensor. First we determined the

model of the platform then we executed the so called

unidirectional square-path test (Borenstein, 1996).

The model of the navigating agent and the

connection between the motion and the measured

sensor values is shown on figure 3/b. The y

component measures orientation change, the linear

dislocation appears in the value of the x component.

Figure 3: The test platform and its model.

By normal conditions the measured error was

relatively small in the course of movement around

the square. 4,5 meters of travel yielded 0,01-0,02

meters of the inaccuracy (meaning 0,3% overall

error).

Control

unit

LEDs

Telecentric Optics

Mouse-chi

Microcontrolle

a.) c.) b.) d.) e.)

From above From under

b.) a.)

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It is important to add that slip free wheel contact

was supposed. In case of slippage the error grew

dramatically, the inaccuracy could even reach the

0,3-0,4 meters (7% error). It was caused by the

appearance of linear dislocation in the value of the y

component, and the ratio between the linear

dislocation and the orientation change can not been

determined.

It seems to be a paradox situation since this

optical navigation sensor was developed for

replacing the wheel encoder based odometry,

however we faced the same problem (the inaccuracy

from the slippage of the wheels). But as opposed to

the odometry, out experiences showed, that this

method has a simple and effective solution. By using

two optical sensors, a platform independent an

accurate dead reckoning navigating sensor-system

can be developed that works well under real

conditions (wheel-slippage, travel over uneven floor

etc.) too.

4 ALTERNATIVE APPLICATIONS

4.1 Electronic Stability Control of

Vehicles

One of the most critical areas of stability systems

(ESP, ABS etc.) is the accurate measurement of the

dynamic state of the vehicle. Probably the most

important state variable is the slip angle of the

moving vehicle. It is needed for the calculation of

the force between the wheels and the ground and

also for the exact description of the motion-state.

Presently used measurement procedures of the

slip angle are in general very expensive therefore it

they are not really usable in mass production

(Kálmán, 2005). Hence it is necessary to use

estimation methods to provide the value of the slip

angle. Unfortunately these procedures can not

provide accurate results (Bári, 2006).

This optical sensor provides an effective and

cheap solution to solve the problem of the slip angle

because it can measure (instead of estimation)

directly the slip angle, the ground velocity in 2D and

other important motion information. Therefore it can

be an additional sensor of any vehicle handling

enhancement system, for example ABS or ESP.

Naturally for utilization in the automotive industry it

is necessary to fulfil numerous standards and

reliability requirements, which will not be discussed

in this paper.

4.2 Linking Vehicle Dynamic Properties

into World Map Building

Another interesting area of application is a multi

sensor navigation unit of a mobile robot or

automated vehicle. By fusing information from the

optical correlation sensor and other sensors

measuring the vehicles inner state - such as

acceleration, tilt, wheel speed, GPS sensors - very

precise dynamical information can be obtained. This

information can be used to build a world map that

contains information not only about the environment

but also its effects on the dynamical behaviour of the

robotic vehicle. The optical sensor plays an

important role in this unit because it is able to

measure true ground velocity relative to our vehicle,

making us able to determine wheel slippage and slip

angle.

Intelligent mobile robots navigate around in their

environment by gathering information about their

surroundings. The most common approach is to use

ranging sensors mounted on the robot to form

occupancy grids or equivalent. Other approaches

avoid this metric division of space and favour

topological mapping. By combining these mapping

techniques it is possible to form a hierarchical map

that has the advantages of both methods while some

of the disadvantages can be avoided (Thrun, 1998).

Occupancy grids classify the individual cells

based on range data and possibly other features such

as colour or surface texture or variation. This

becomes very important in outdoor mobile robotics

when the robot needs to distinguish between real

obstacles and traversable terrain. An extreme case is

given by navigation in a field of tall grass. The

elevation map will represent the scene as a basically

horizontal surface above the ground level; that is, as

a big obstacle in front of the vehicle. It is apparent

that only by integrating the geometry description

with terrain cover characterization will a robot be

able to navigate in such critical conditions (Belluta,

2000).

Topological maps describe the world in terms of

connections between regions. This is usually enough

indoors, or in well structured environments, but

when travelling through more complex terrain a

different representation might be necessary. For

example a sloping gravel road or sand dune may

only be traversable at a certain speed or only one

way, up or downwards. By applying information

from the inertial navigational unit, such as slope

angle, wheel slippage, actual movement versus

desired movement, these characteristics can be

learned (or used from apriori information) and the

OPTICAL NAVIGATION SENSOR - Incorporating Vehicle Dynamics Information in Mapmaking

273

connections of the topological graph can be updated

accordingly.

Terrain characteristics (and those of our vehicle)

determine the maximum safe speed, braking distance

curve radius at a given speed, climbing manoeuvres

etc. It is obvious that the more information we have

about a certain region we are planning to travel

through, the more driving efficiency we can achieve,

as it is generally unsafe to drive at high speed

through bumpy terrain or make fast turns on a

slippery surface. By incorporating the data from the

navigational unit into the world map, we can

associate driving guidelines to a given map segment.

Also on the higher, topological level - using apriori

information - we can identify the type of the terrain

for a given point of our topological graph, as office

environment, forest, urban area, desert etc. By doing

so, we narrow down our choices when making

decisions about terrain coverage. For example it is

unlikely to encounter sand, water or foliage in an

office environment. If we know the type of terrain

ahead we can make a more accurate estimate of the

driveability of the area thus increasing driving

efficiency.

In this section a hierarchical map making method

was proposed which uses data from a multi-sensor

navigation unit that supplies information about

vehicle dynamics. This unit heavily relies on the

optical correlation sensor described in the preceding

sections. By measuring wheel slip and vehicle slip

angle we are able to associate drivability guidelines

such as safe speed, friction coefficient, minimal

driving speed etc. to a given map segment or type of

terrain. A higher level of environment recognition

was also proposed: based on apriori information, or

sensor data the vehicles control system decides the

type of environment (e.g. office, forest, desert) the

robot traverses at the time, and changes the

probability of terrain types, characteristic of the type

of environment, thus simplifying terrain

classification.

5 SUMMARY

In the previous sections we presented an optical

navigation sensor which measures motion

information (velocity, dislocation, slip angle)

without ground contact.

An alternative dead reckoning technique is

proposed in section

3, that yields superior accuracy

compared to wheel encoder based methods. In the

first part a short overview of principle (e.g. optical

flow) is given followed by the description of our

experimental setup. Experimental results are given

in the last part. Conclusions: the current system

should be made platform independent by using two

rigidly linked sensors, use of laser mouse chips is

recommended to overcome the problem of texture

dependency.

Finally examples of alternative application areas

were presented: slip angle measurement for the

safety systems of vehicles and hierarchical map

building with additional driveability information.

As a conclusion we can say that the device and

dependent methods presented here can serve as

cheap and accurate alternative solutions to numerous

problems of the robot and automotive industry.

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