An Approach to Marker Detection in IR- and RGB-images for an

Augmented Reality Marker

Aaronkumar Ehambram, Patrick Hemme and Bernardo Wagner

Institute of Systems Engineering - Real Time Systems Group, Leibniz Universit

at Hannover,

Appelstr. 9A, 30167 Hannover, Germany

Keywords:

Computer Vision, Augmented Reality, Kinect, Intel RealSense, Pose Estimation, Retroreﬂective Markers,

Sensor Fusion.

Abstract:

We introduce an augmented reality marker based on ArUco markers (Garrido-Jurado et al., 2014) that can be

detected in RGB- and IR-images by using retroreﬂective material. Due to active perception by IR-capable

camera systems the negative impact of external disturbances like change of light conditions on the marker

detection is minimized. By the parallel processing architecture of RGB- and IR-images redundancy stabilizes

the detection. As different retroreﬂective materials inﬂuence the image quality depending on the camera

system, we also examined different retroreﬂective materials and compared the performance of the Kinect V2

and the Intel RealSense D435 regarding the detection probability depending on the geometrical distance of the

augmented reality marker to the camera.

1 INTRODUCTION

Localization, pose estimation and mapping are some

of the most important tasks for autonomous systems.

Applications like autonomous robots (Sim and Lit-

tle, 2006) and unmanned vehicles (Broggi and Dick-

manns, 2000) are highly dependent on the reliability

of pose estimation tasks that can be solved effectively

by using cameras. Pose estimation algorithms that use

natural features such as keypoints and texture often

need signiﬁcant computational requirements and are

subject to ﬁlter stability issues. Using artiﬁcal land-

marks reduces to a certain degree the need of com-

putational requirements due to specialized processing

steps that lead to an optimized extraction of the points

of interest. Additionally, the usage of artiﬁcial land-

marks enables us as users also to adapt the markers

to our needs. An improvement in pose estimation

based on artiﬁcial landmarks requires a very stable

and precise recognition of the markers. One way of

gaining stability and precision is redundancy. Many

robotic systems as described in (Fankhauser et al.,

2015) are equipped with cameras that provide in ad-

dition to monocular and stereo vision depth-images

and IR-images. Widely spread camera systems like

the Kinect (Zhang, 2012) and the Intel RealSense

(Draelos et al., 2015) use such technologies making

a huge bundle of sensor data available. The usage

of more than one sensor as a source of information

for recognition will lead to a more stable detection.

In this paper we suggest a new physical topology of

augmented reality markers based on ArUco markers

(Garrido-Jurado et al., 2014) that enable a detection in

the RGB- and IR-image by using retroreﬂective ma-

terials. Therefore, we examined two different retrore-

ﬂective materials and evaluated their impact on the

image data provided by two different cameras - the

Kinect V2 and the Intel RealSense D435. In our eval-

uations we concentrate on the detection quality de-

pending on the distance of the markers to the cam-

era. The main contribution of this paper is the pro-

posal of an optical ﬁducial augmented reality marker

detection based on RGB- and IR-image data. In the

following we ﬁrst discuss related works in Section 2,

then we present our suggested physical marker topol-

ogy based on the ArUco markers and shortly picture a

parallel architechture enabling the detection in RGB-

and IR-image in Section 3. In Section 4 we describe

our evaluation system and discuss the experimental

results and end with conclusions in Section 5.

2 RELATED WORK

In the literature several ﬁducial marker systems have

been proposed. Some of these are discussed in the

190

Ehambram, A., Hemme, P. and Wagner, B.

An Approach to Marker Detection in IR- and RGB-images for an Augmented Reality Marker.

DOI: 10.5220/0007810301900197

In Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019), pages 190-197

ISBN: 978-989-758-380-3

following in order to determine the goals of this pa-

per. Some approaches use planar circular markers

that encode the identiﬁcation in circular sectors or in

concentric rings (Naimark and Foxlin, 2002). Due

to their primitive construction such markers usually

provide only one correspondence point located at the

center. That is why more than one marker is needed

for pose estimation. Motion Capture is a marker sys-

tem using retroreﬂective materials. The markers are

often spheres (Rossner, 2014) with a retroreﬂective

surface that can easily be used for segmentation in

the IR-image as described in (Dorfm

uller and Wirth,

1998). The usage of such markers is very common

in Motion Capture because of the stability of the de-

tection. As special IR-cameras are used, the marker

system is optimally improved for the detection algo-

rithms. However the identiﬁcation of the markers in-

volves a complex detection process obtained from the

relative marker positions. A very robust alternative

to the previously discussed approaches is a square-

based planar marker system. One of the most popular

square-based ﬁducial marker systems is ARToolKit

(Kato and Billinghurst, 1999). ARToolKit markers

are similarly composed to ArUco markers by a wide

black border with an inner image which is stored in a

database of valid patterns. Although ARToolKit was

extensively used the template matching approach for

marker identiﬁcation leads to high false positive and

inter-marker confusion rates. Also the high sensitiv-

ity to changing light conditions due to ﬁxed global

threshold to detect squares negatively inﬂuences the

detection algorithm. The ARTag system (Fiala, 2010)

is based on the same principal of square-based ﬁdu-

cial systems using binary codes for identiﬁcation. The

improvement of the ARTag system is established by

an edge-based square detection method that is more

robust to lighting and partial occlusion and by a bi-

nary coding scheme that includes checksum bits for

error detection and correction. Unfortunately the pro-

posed marker dictionary is ﬁxed to 36 bits and the

maximum number of erroneous bits that can be cor-

rected is two. The ArUco library (Garrido-Jurado

et al., 2014) provides a stable marker detection based

on the ARToolKit and an algorithm for generating

conﬁgurable marker dictionaries following a criterion

to maximize the inter-marker distance and the number

of bit transitions. The detection is based on contour

extraction and polygone detection. The ArUco detec-

tion algorithm is used in our implementation due to

its accurate performance.

None of the marker systems that use planar markers

make use of special material for detection improve-

ment. As mentioned, on the one hand Motion Cap-

ture systems show a very reliable detection scheme of

the markers due to the use of retroreﬂective material

and IR-cameras. But on the other hand square-based

ﬁducial planar markers enable very robust and com-

pact artiﬁcial landmark detection methods in RGB-

images. That is why we fuse both marker topologies

to one novel marker topology based on ArUco mark-

ers that are able to be detected in RGB- and IR-image

data.

3 RGB- AND IR-IMAGE BASED

MARKER DETECTION

The marker detection proposed in this paper is based

on the ArUco marker detection presented in (Munoz-

Salinas, 2012) and (Garrido-Jurado et al., 2014). Our

proposed marker topology and the parallel processing

architecture are described in the following.

3.1 Enabling IR-image Marker

Detection

In order to use the ArUco detection algorithm with

IR-image data, it is necessary to change the physical

topology of the marker. While in the RGB-image the

colours and contrast of the used material and the light

conditions have the main impact on the image quality,

IR-images captured by active IR-cameras are highly

inﬂuenced by the reﬂection characteristics of the ma-

terial. The higher the reﬂection rate, the brighter the

corresponding spots appear in the image due to high

pixel values. Consequently, the usage of retroreﬂec-

tive material for the white bits on the marker leads to

the desired characteristics. As the black bits of the

markers are equipped with diffuse reﬂecting and the

white bits are coated with retroreﬂective material, the

necessary contrast between the white and black areas

in the IR-image is maintained leading to high gradi-

ents along the contours that have to be extracted. It

stabilizes the detection, since the whole detection al-

gorithm is based on the optimized gradient image.

3.2 Parallel Processing of RGB- and

IR-image

As the marker detection with retroreﬂective material

works with RGB- and IR-image data, it is expedi-

ent to use both data in order to stabilize the detection

through redundancy. In Figure 1 the ROS (Quigley

et al., 2009) based architecture of our fused detec-

tion is depicted which we describe in the following in

detail. In Figure 1 the involved nodes that represent

the processes that run simultaneously are marked with

An Approach to Marker Detection in IR- and RGB-images for an Augmented Reality Marker

191

Figure 1: Fused marker detection from the RGB- and IR-image.

circular boundaries. The corresponding topics are

marked in rectangular boundaries. In total three nodes

are necessary for parallel processing of the RGB- and

IR-image: Two nodes process the ArUco-marker de-

tection. The one applies the detection algorithm on

the RGB-image and the other on the IR-image. For

the detection algorithm camera parameters, transfor-

mations and the image data have to be provided. The

output of the detection nodes are the detected marker

positions and their identiﬁcation numbers. At this

point four different constellations are possible:

1. In both images no markers were detected.

2. In both images markers were detected.

3. Only in the RGB-image markers were detected.

4. Only in the IR-image markers were detected.

Regarding the ﬁrst case, no further processing steps

are necessary. In the second case two marker posi-

tions for one corresponding marker in the scene are

published. Ideally, both published poses are equal.

But due to sensor inaccuracy and noise both poses

are not always equal. In order to calculate the correct

pose of the marker a further node is used, that fuses

the different marker poses. As RGB- and IR-cameras

have a similar detection accuracy when retroreﬂective

markers are used (see Section 4), neither the RGB-

nor the IR-camera based detection should be favored.

Consequently, the correct pose of the marker is calcu-

lated by averaging the published pose data of the cor-

responding markers. Accordingly if the RGB-image

based pose is described by

RGB

= (

RGB

)

(1)

and the IR-image based pose is described by





(2)

the published pose data is

p =

RGB

+ p

. (3)

The calculated pose is then published to the corre-

sponding topic. The third and the fourth case de-

scribe the situations in which the marker fusion node

subscribes to the detected marker topics but no corre-

spondences between both data can be determined. Be-

cause of the high true-positive accuracy of the ArUco

marker detection it is expedient to publish the de-

tected pose of the corresponding marker to the de-

tected marker topic.

4 EVALUATION

For evaluating the performance of the detection of the

marker presented in Section 3 we measured the de-

tection probability depending on the distance of the

marker. For sake of comparability and evaluation

of the degree of detection performance improvement

we also printed the ArUco marker on common white

printing paper. All markers have a border size of

15 cm. In order to perform a reproducible measure-

ment of the detection probability we built a measuring

system which is pictured in Figure 2. The marker is

ﬁxed on a frame that is mounted on a linear rail sys-

tem. The angle of perspective in respect to the cam-

era system is variable. The camera angle in respect to

the normal vector of the ﬂoor was measured to 30

◦

In order to maintain comparability of the different

cameras, identical environmental conditions are nec-

essary. Therefore, both cameras are mounted on the

measuring system and the camera data is recorded in

parallel. In order to record the distance of the marker

the frame is equipped with two poles, that can be de-

tected by the laser scanner mounted on the measuring

Figure 2: Setup for measuring the detection probability.

ICINCO 2019 - 16th International Conference on Informatics in Control, Automation and Robotics

192

system with the cameras. The detection probability

is approximated by a sampling method. Therefore,

the length of 2.5 m of the linear rail system is quan-

tized into 10 cm steps. Each step represents a bin.

For generating the samples both cameras are run with

30 f ps while the frame of the rail system, on which

the markers are mounted, is slowly moved back and

forth varying the distance between marker and cam-

eras. As the distance is tracked by the laser scan data

during the whole experiment the amount of catured

frames F

captured,i

, the amount of frames in which the

markers are detected F

detected,i

and the corresponding

distance d

can be assigned to each bin B

. Conse-

quently, B

can be described by

= {F

captured,i

, F

detected,i

, d

}. (4)

The detection probability p

) for each bin B

can be

approximated by dividing the amount of the frames

in which the markers are detected by the number of

captured frames according to equation 5

) =

detected,i

captured,i

. (5)

Three different parameters have strong inﬂuence on

the detection probability measurement depending on

the distance:

1. The markers tilt angle changes the reﬂection be-

haviour of the materials.

2. The different retroreﬂective materials lead to dif-

ferent detection performances.

3. The cameras show different behaviours and pro-

vide very different images.

For investigating the inﬂuence of the tilt angle three

different angles were chosen for the experiments that

are explained in Subsection 4.1 in more detail. We

used two different retroreﬂective materials for the ex-

periments that have different reﬂection characteris-

tics. For better comparability paper printed ArUco-

markers are tested additionally. For reader con-

venience we want to introduce the terms Retro 1

and Retro 2 for the different retroreﬂective materi-

als. As such retroreﬂective materials are used in road

construction the reﬂectance characteristics are deter-

mined by technical regulations. The following men-

tioned technical reﬂectance data are based on the ger-

man regulations ”Richtlinien fuer die Sicherung von

Arbeitsstellen an Strassen” - in short RSA (Schoen-

born and Schulte, 1995). Retro 1 is a microprisms

based material that leads to higher reﬂectance values

than foils with glass bead construction, which results

in better viewing of microprismatic signs in the dark.

The Retro 1 material is a foil that is constructed with

a 3M pattern and C construction type. The reﬂectance

behaviour can be assigned to the reﬂectance class

RA1 according to (Schoenborn and Schulte, 1995).

Retro 2 is a material with encapsulated catadioptric

acting glass beads, which are partially embedded in

a plastic layer. The Retro 2 material is a foil that

is constructed with a 3M pattern and B construction

type. The reﬂectance behaviour can be assigned to

the reﬂectance class RA2 according to (Schoenborn

and Schulte, 1995). In our experimental investiga-

tions we used the Kinect V2 camera and the Intel

RealSense D435. While the Intel RealSense D435

shows economical beneﬁts the used infrared projec-

tor uses structured-light that negatively inﬂuences the

marker detection in the IR-image. Due to the time-of-

ﬂight technology of the Kinect and the homogenous

infrared projection the IR-image data provided by the

Kinect leads to an improved detection behaviour. But

the time-of-ﬂight technology enables only the percep-

tion of infrared light that has the particular frequency

modulation that matches the emitter frequency. Con-

sequently, the Kinect does only percept the infrared

light that it projects with its emitter into the scene. In

contrast the Intel RealSense D435 also percepts the

surrounding infrared light enabling the usage of ex-

ternal light projectors. Although the Intel RealSense

D435 is more ﬂexible external disturbances by IR-

emitters may lead to unwanted noise. In the following

the experimental results will be presented in detail.

4.1 Markers Tilt Angle

In order to measure the inﬂuence of the tilt angle the

different markers are mounted on the frame that is

shown in Figure 2. For each marker both cameras

are used for detecting the markers. Since the angle

of perspective of the cameras in respect to the normal

vector of the ﬂoor was measured to 30

◦

the markers

tilt angle is set to 0

◦

, 15

◦

and 30

◦

in respect to the

tangent vector of the ﬂoor level. Due to very similar

results of the 18 experiments (3 markers times 2 cam-

eras times 3 tilt angles) only one parameter constella-

tion will be discussed in the following. The diagram

portrayed in Figure 3 shows the results of the tilt angle

experiments for the Kinect and the Retro 1 marker.

The abscissa measures the horizontal distance of the

marker to the Kinect camera and the ordinate shows

the detection probability determined according to the

method described in Section 4. As the IR- and RGB-

images are captured synchronously the frame counter

is increased if both data are received. A frame is

marked as a detected frame if either in the IR- or in

the RGB-image markers are found as decribed in Sec-

tion 3.1. The results of three different experiments are

integrated into the plot 3. The red graph corresponds

An Approach to Marker Detection in IR- and RGB-images for an Augmented Reality Marker

193

Figure 3: Detection probability depending on the tilt angle

of marker Retro 1 for the Kinect.

to the detection probability for a tilt angle of 0

◦

, the

green graph to 15

◦

and the black graph to 30

◦

. Ac-

cording to the plot 3 the marker detection shows the

best performance with a tilt angle of 30

◦

. While the

detection with a tilt angle of 30

◦

has 100 % accuracy

till a distance of 2.4 m the 100 % detection probabilty

for 15

◦

is beneath 1.5 m and for 0

◦

beneath 0.75 m. As

for all experiments the tilt angle of 30

◦

shows the best

performance we ﬁx the parameter to 30

◦

for further

discussion.

4.2 Retroreﬂective Materials and

Cameras

For determining the best camera-material-

constallation providing the best performance in

respect to the marker detection we want to present the

experimental results to the investigations to the dif-

ferent marker materials and cameras. Therefore, two

different environmental conditions are considered

in detail. While diffused daylight 4.2.1 disturbs the

IR-camera of the Intel RealSense D435 as described

in Section 4 and reﬂection negatively inﬂuences the

RGB-image data the experiments in an underexposed

room in Section 4.2.2 stress the beneﬁts of using

IR-camera data and show the behavior of the different

retroreﬂective materials in respect to the cameras.

4.2.1 Diffused Daylight

The experimental results shown in Table 1 and 2 were

recorded during diffused daylight conditions. While

the Table 1 shows the detection probability of the dif-

ferent camera-material-constellations, Table 2 shows

snapshots of the markers captured by the respective

cameras. Each cell of the chart 1 is made of similar

diagrams: The abscissa measures the horizontal dis-

tance of the marker to the respective camera and the

ordinate shows the detection probability determined

according to the method described in Section 4. The

green graph describes the detection probability of the

markers based on the RGB-image, the black graph

refers to the IR-image and the red graph shows the de-

tection probability when both frames are parallel con-

sidered as mentioned in Section 3.1. The RGB-image

based marker detection with the Kinect is according

to Table 1 independent of the material. This fact is

also depicted in Table 2. As all RGB-images have a

very similar appearance the edge based detection al-

gorithm performs very similar. In contrast, the IR-

image based marker detection performance strongly

depends on the marker material: While the ArUco-

marker printed on paper is detected very accurately

till a distance of 1.5 m, the Retro 1 marker is detected

till a distance of nearly 2.5 m though the detection is

not as accurate as with paper due to the lower detec-

tion probability. The Retro 2 marker has the worst

perfomance regarding IR-image based detection. The

very inaccurate detection is caused by the reﬂection

characteristics of the encapsulated catadioptric act-

ing glass beads. As pictured in Table 2 black spots

appear in the regions where the retroreﬂective mate-

rial is located and due to the inhomogene bright re-

gions the detection algorithm fails. Concerning the

results of the Kinect camera in respect to the merged

marker detection it is obvious that using the Retro

marker shows the most beneﬁts due to the very accu-

rate RGB-image detection and the capability to detect

the marker in far distances based on the IR-image.

The Intel RealSense has similar detection results re-

garding the RGB-images. But in Table 2 we can see

that the detection probablilty based on RGB-images

is clipped to 0 % below a distance of 0.5 m. This be-

haviour is caused by the experimental setup due to

the differing position of the Intel RealSense in respect

to the Kinect. Besides the neglectable impact caused

by the setup the RGB-image based detection is sim-

ilarly accurate as with the Kinect. But the IR-image

based detection shows its best performance with pa-

per. Both retroreﬂective materials lead to poor results

with maximum distances of 1.2 m. The reason for

the described detection behaviour is shown in Table

2: Due to the structured-light technology of the Intel

RealSense surrounding infrared light is also percepted

by the camera. Consequently, the diffuse reﬂecting

paper appears in the IR-image accurately, leading to

good detection. But both retroreﬂective materials are

over illuminated, so that the inner structure can not

be extracted and the detection fails. Conclusively,

the Intel RealSense in combination with paper printed

ArUco markers has the best performance under the

diffused daylight conditions.

4.2.2 Underexposed Room

The experimental results of the detection probability

in a low illuminated room are described in the Tables

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194

Table 1: Detection probability measurement results with diffused daylight.

Kinect Intel RealSense

paper

Retro 1

Retro 2

Table 2: Detection probability measurement pictures with diffused daylight.

Kinect RGB Kinect IR Intel RealSense

RGB

Intel RealSense

paper

Retro 1

Retro 2

An Approach to Marker Detection in IR- and RGB-images for an Augmented Reality Marker

195

Table 3: Detection probability measurement results in an underexposed room.

Kinect Intel RealSense

paper

Retro 1

Retro 2

Table 4: Detection probability measurement pictures in an underexposed room.

Kinect RGB Kinect IR Intel RealSense

RGB

Intel RealSense

paper

Retro 1

Retro 2

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196

3 and 4. The results of the Intel RealSense reveal that

in no frames markers were detected. The reason for

the very poor detection performance based on the data

provided by the Intel RealSense can be seen in Table

4: Due to the reduced illumination in the room also

the infrared light by external emitters is reduced, so

that the structured-light dominates in the IR-images.

That is the reason why the markers are pictured with

bright dots. As the detection algorithm is based on

the gradient image, the necessary edges for extracting

the marker are not calculated appropriately. Accord-

ing to the expectations the RGB-image based detec-

tion does not work ﬁne with low illumination. But

interestingly the Kinect still detects the markers since

a small light emitter was placed in the room. Conse-

quently, the exposure adjustment of the Kinect sup-

ports the marker detection. The Kinect shows a rea-

sonable performance with the Retro 1 marker regard-

ing the IR-image. The detection behaviour with IR-

images of the paper printed marker in the underex-

posed room is similar to the results described in Table

1. The detection of the Retro 2 marker is due to the

already explained effects of the reﬂectance not accu-

rate.

Conclusively the best marker detection performance

can be achieved with the Kinect camera and the

Retro 1 marker. Due to the very accurate detection

with daylight and the reasonable behaviour on the IR-

image data a parallel detection in both images and a

following merging step stabilizes the detection due to

redundancy.

5 CONCLUSION

We presented an augmented reality marker based on

ArUco markers that can be detected in RGB- and

IR-images. The usage of retroreﬂective material im-

proves the marker detection in IR-images remarkably.

Due to active perception by IR-capable camera sys-

tems the negative impact of external disturbances like

change of light conditions on the marker detection is

minimized. As different retroreﬂective materials in-

ﬂuence the image quality depending on the camera

system, we also examined two different retroreﬂec-

tive materials and compared the performance of the

Kinect V2 and the Intel RealSense D435 regarding

the detection probability depending on the distance of

the augmented reality marker to the camera. Accord-

ing to our results the Kinect together with the micro-

prisms based material for the markers shows the best

detection performance. Since the marker detection is

parallel processed on the IR- and RGB-image, it is

stabilized due to redundant sensor fusion.

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