Real-time Cargo Volume Recognition

using Internet-connected 3D Scanners

Felix Föcker

, Adrian Neubauer

, Andreas Metzger

, Gerd Gröner

and Klaus Pohl

Paluno - The Ruhr Institute for Software Technology, University of Duisburg-Essen, Gerlingstr. 16, 45127 Essen, Germany

Carl Zeiss Vision GmbH, Turnstr. 27, 73430 Aalen, Germany

Keywords: 3D Volume Recognition, Mobile Devices, Internet-connected Sensors, Transport and Logistics.

Abstract: Transport and logistics faces fluctuations in cargo volume that statistically can only be captured with a large

error. Observing and managing such dynamic volume fluctuations more effectively promises many benefits

such as reducing unused transport capacity and ensuring timely delivery of cargo. This paper introduces an

approach that combines user-friendly mobile devices with internet-connected sensors to deliver up-to-date,

timely, and precise information about parcel volumes inside containers. In particular, we present (1) RCM, a

mobile app for unique identification of containers, and (2) SNAP, a novel approach for employing internet-

connected low-cost, off-the-shelf 3D scanners for capturing and analyzing actual cargo volumes. We have

evaluated the accuracy of SNAP in controlled experiments indicating that cargo volume can be measured

with high accuracy. We have further evaluated RCM together with SNAP by means of a survey study with

domain experts, revealing its high potential for practical use.

1 INTRODUCTION

The execution and management of transport and

logistics processes strongly benefits from up-to-date,

timely, and precise information about transported

goods. Current technology developments such as

internet-connected devices (Internet of Things),

mobile and cloud computing, as well as software-

based web services provide unprecedented opportu-

nities for collecting relevant information in real-

time. For example, sensors can be placed at relevant

positions along a transport chain and even be at-

tached to the transport containers and transported

goods, thereby delivering real-time data about cargo.

Such data can in turn be conveyed to downstream

logistics partners, allowing them to better plan and

manage the delivery of goods, match the actual car-

go volume to the cargo capacity more precisely,

consolidate shipments to benefit from better freight

rates or increased cargo security, and, in general,

more effectively plan transport and handling activi-

ties for the enterprise (Metzger et al. 2014).

This paper focusses on parcel transport and lo-

gistics as one area where real-time information fos-

ters better, even proactive, reaction to deviations.

Parcel transport faces frequent volume fluctuations.

For example, the amount of parcels that should be

transported from an online reseller to its customers

may deviate from the planned amount of parcels for

that day, as actual parcel volumes can statistically be

captured only with large error. Observing and man-

aging such dynamic volume fluctuations more effec-

tively promises many benefits for a transport and

logistics enterprise, such as reducing unused

transport capacity and ensuring timely delivery of

goods.

Our contribution is to combine user-friendly mo-

bile devices with internet-connected sensors for

delivering up-to-date, timely, and precise infor-

mation about parcel volumes. In particular, we pre-

sent

(1) RCM (Roll Container Manager), an An-

droid app for the unique identification of parcel-

containers, and (2) SNAP (Scan and Analyse Parcel-

Containers), a novel approach for employing inter-

net-connected low-cost, off-the-shelf 3D scanners to

measure parcel volumes. RCM matches data ob-

served by the 3D scanner to the parcel-container,

thus providing unique identification of the container

for effective process management

3D scanners are one promising class of sensors

that can be applied to “estimate” volumes of spatial

Additional information about RCM, SNAP

and its quantitatively evaluation is provided at

https://sites.google.com/site/snaprcm

323

Föcker F., Neubauer A., Metzger A., Gröner G. and Pohl K..

Real-time Cargo Volume Recognition using Internet-connected 3D Scanners.

DOI: 10.5220/0005377203230330

In Proceedings of the 10th International Conference on Evaluation of Novel Approaches to Software Engineering (ENASE-2015), pages 323-330

ISBN: 978-989-758-100-7

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

objects. Volume scanning using 3D has been applied

in various areas but mostly using proprietary, com-

plex, and costly 3D scanning devices. Therefore,

systems using low-cost 3D scanners promise to be

an attractive alternative to industrial sensor systems

due to their lower capital expenditures and their

capability of efficient process integration (Kückel-

haus et al. 2013). The quality of existing low cost

3D scanners has reached a level that makes it feasi-

ble to apply them in industrial settings. Successful

applications of such scanners have been reported in

cases such as warehouse picking operations (Xing-

yan Li et al. 2012), humanoid driving (Rasmussen et

al. 2013), or object reconstruction (Izadi, New-

combe, et al. 2011; Zhou and Koltun 2013; Xu et al.

2012; Bondarev et al. 2013; Kainz et al. 2012).

To quantitatively assess the accuracy of parcel

volume recognition as facilitated by SNAP, we per-

formed controlled experiments. Results indicate that

the position of the parcel-container and its payload

influence the accuracy. In the best case, we were

able to achieve accuracy with an average error rate

of about -15%. Complementing this quantitative

evaluation of our solutions, we performed qualitative

evaluations of SNAP and RCM through a survey

study with domain experts, indicating its high poten-

tial for practical use. Especially, knowledge about

the container volume in operative transport man-

agement was perceived as high importance.

The remainder of the paper is structured as fol-

lows. Section 2 gives an overview of the foundations

for 3D scanning. Section 3 relates our contribution

to the state of the art. Section 4 describes the imple-

mentation of SNAP and RCM. Section 5 presents

and discusses the design, execution, and results of

our quantitative evaluation. Section 6 describes our

qualitative evaluation.

2 FOUNDATIONS

We employ Microsoft Kinect as 3D scanner (con-

cretely, Microsoft Kinect for Windows v1 (Mi-

crosoft 2014)) and therefore present its main founda-

tions as basis for the remainder of the paper.

Kinect solutions have previously been experi-

mentally evaluated against professional solutions

concerning accuracy, effective field of view, and

object detection (Smisek et al. 2011; Khoshelham

2011; Dutta 2012). In (Dutta 2012) Kinect solutions

have been reported to perform better in some aspects

than professional solutions.

With the release of the Kinect for Windows SDK

v1.7, Kinect Fusion was introduced, enabling the

calculation of 3D-volume from depth stream data

(Izadi, Newcombe, et al. 2011; Izadi, Kim, et al.

2011; Microsoft Developer Network 2013). Several

depth-pictures are integrated step-by-step to obtain a

3D representation of the depth information (New-

combe et al. 2011; Freedman et al. 2012), and then

can be exported as a 3D-object. The raw-data of

such a 3D-object can be exported in form of a point-

cloud, an accumulation of points in a 3D space with

x, y and z coordinates, and can be processed further.

For example, raw-data is used to identify objects in a

scene by comparing two point-clouds and identify-

ing the changed points (Litomisky and Bhanu 2013)

or by creating silhouettes from the color and depth

data to separate objects from the scene (Xu et al.

2012). We use both ideas in our work to separate

parcel-containers from their background.

The Kinect device is very cost effective, but has

its own limitations. First, Kinect Fusion requires a

graphics processor for real time image processing,

which means the graphics hardware and memory can

become a restrictive factor (Microsoft Developer

Network 2014). Depending on the point cloud reso-

lution, scenes up to multiple cubic meters can be

scanned. Second, the depth sensor has noise and a

limited range. Several algorithms address these limi-

tations, e.g., (Bondarev et al. 2013; Roth & Vona

2012; Whelan et al. 2012; Zeng et al. 2012).

3 RELATED WORK

In this section, we describe approaches related to our

work.

With depth data available from 3D scanners, var-

ious application problems can be addressed. For

example, (Xingyan Li et al. 2012) present an imple-

mentation for the visual detection of objects in a

warehouse order picking process. A Kinect sensor is

used to identify goods each time an item is placed in

a basket. Both the basket and the goods are static.

The color images and depth images of the Kinect are

used to compare textures and geometric shapes of

detected objects against known objects in a database.

Recognition rates approaching 100% have been

achieved. However, the solutions does not employ

the new Kinect Fusion APIs. The authors use a

method for calculating the volume of objects located

within the basket. The depth of the container is

compared with the height of the measured object

points and the difference represents the height of the

object, which is then multiplied by the convex hull.

A project similar to our work was carried out by

DHL. Two low-cost depth sensors (similar to Kinect

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sensors) were used to measure the volume of cargo

on pallets. The results of the pilot phase indicated

high accuracy (Kückelhaus et al. 2013). Although

the project of DHL is a very similar scenario, it is

significantly different in structure from the applica-

tion considered in our work. In the project, the load-

ing volume of pallets is measured. Pallets have the

advantage that all sides are open. Due to the fact that

two sensors are used, a complete 3D capture of the

objects to be measured can be performed. From the

data, for example, the volume of the point cloud can

be calculated with the Delaunay triangulation (De-

launay 1934). In our work, only one sensor and

parcel-containers with only one open side are used.

Thereby, the Delaunay triangulation cannot be ap-

plied to the cloud point, because a full coverage

cannot be ensured, yet the space has to be calculated

within the container. These circumstances make it

more difficult to capture volume in our case.

4 IMPLEMENTATION

In this section, we briefly describe the implementa-

tion of the 3D scanning solution SNAP and the An-

droid App RCM.

Overview of SNAP. Our implementation for

volume scanning employs a Microsoft Kinect 3D

scanner for real-time volume recognition of parcel-

containers, called SNAP (Scan and Analyze Parcel-

Containers). For the technical setting, the Kinect

sensor is mounted on a tripod slightly above the

parcel-container to attain a complete overview of the

payload. The key idea for the volume calculation is

to use the point cloud of the scene, provided by the

Kinect Fusion algorithm and based upon the depth

stream of the Kinect sensor, to extract the point

cloud of the parcel-container and calculate its vol-

ume.

Our process for volume recognition consists of

two activities with three steps each: (cal) calibration

and (scan) actual scanning (see Figure 1). During

the calibration activity, the background and also an

empty parcel-container are scanned as baseline. The

calibration of the background is a simple snapshot of

the point cloud of the scene without a parcel-

container in it (see cal-1 in Figure 1), and is used for

the algorithms for volume recognition, as described

in detail below.

To achieve precise measurements of volumes,

our algorithm performs two essential steps both

during calibration and scanning. First, the point

cloud of the parcel-container is scanned (cal-2 /

scan-1). Second, the points of the container are

extracted from the scene by filtering out the back-

ground (cal-3 / scan-2). Based on these intermediate

steps, the volume of a loaded parcel-container can

finally be calculated by simply subtracting the vol-

ume of the empty parcel-container from the volume

of the loaded container (scan-3).

Overview of RCM. In addition to SNAP, we

developed the Android application Roll Container

Manager (RCM) to control the Kinect and to inte-

grate the prototype in a user-friendly way into given

business processes to perform the scan process. As

mentioned above, such integration requires a unique

identification of the parcel-containers. This can be

cal-1

scan background

cal-2

scan empty

roll container

cal-3

extract empty roll

container and

calculate volume

scan-1

scan loaded

container

scan-2

extract loade d roll

container and

calculate volume

scan-3

calculate cargo

volume

rcm-1 ide ntify container

rcm-2 start scan rcm-3 confirm results rcm-4 scans overview

SNAPRCM

Figure 1: Abstract process for volume recognition.

Real-timeCargoVolumeRecognitionusingInternet-connected3DScanners

325

done via the app by leveraging human-in-the-loop

knowledge (e.g., to make sure that the 3D scanner

indeed scans the parcel-container of interest).

To integrate volume recognition into a given

business process, users are able to control the scan-

ning of a loaded parcel-container by performing

three steps within the RCM-App. First, they identify

the container by scanning a QR-Code (rcm-1 in

Figure 1) to ensure correct data-matching to the

business-process. Following this, SNAP can be

started from the app to scan and analyze the volume

of the payload of the parcel-container (rcm-2). This

also ensures that the containers are only scanned

after their loading has been completed. The results

of volume scanning are directly sent to the RCM and

is visualized for immediate confirmation by users.

By confirming the results (rcm-3), users indicate

they have checked the correctness of results. Results

are then made available to the dispatcher responsible

for process management (dispatchers in turn may

employ control centers for process management). In

addition, users may access an overview of the scans

(rcm-4) where they can compare planned and

scanned container amounts in a chart and review the

details of past scans in a card view.

5 QUANTITATIVE EVALUATION

In the following, we discuss the design, execution,

and results of the conducted quantitative evaluation

of SNAP.

5.1 Experimental Design

We follow the definition of (Prechelt 2001) and

distinguish between dependent, independent, and

confounding variables. Our goal is to measure accu-

racy (relative error of measurement). We will inves-

tigate how the dependent variables are influenced by

(variations of) the independent variables and wheth-

er there are confounding variables that might indi-

cate wrong estimations.

5.1.1 Variables in the Experiment

Dependent Variable. We observe one dependent

variable during our experiment:

 Accuracy of the recognition results. It is meas-

ured in the form of the relative error of meas-

urement:

error 

measuredvolume  realvolume

realvolume

(1)

Independent variables. During the experiment,

three independent variables are systematically varied

to analyze their influence on the dependent variable:

 Horizontal distance between the parcel-container

and the Kinect sensor. The analysis of this varia-

ble is crucial in practical settings since it takes

some effort for the user to take care of the exact

position when containers are scanned.

 Angle of the parcel-container to the Kinect sen-

sor. This is measured as follows: The vertical

line from the Kinect to the ground is the basis.

From this point, we build a radian measure to

span the field of view of the Kinect sensor. The

angle from orthogonal lines of the radian meas-

ure to the container front is the independent vari-

able. For the sake of simplicity, we took only one

line in the center of the Kinect field of view.

 Loading volume, i.e., we want to measure wheth-

er and how the actual loading volume influences

the accuracy of volume recognition.

5.1.2 Variation of Independent Variables

In order to control the variation of the independent

variables, we determined the granularity of changes

and the order in which the variables are modified.

Granularity. For each of the independent varia-

bles, we determined the levels for which we want to

observe the influence on the dependent variable. The

number of levels has been chosen such that we ob-

tain high significance along with feasible experi-

mental efforts (see Table 1).

For the horizontal distance of the container, the

maximal and minimal levels have been restricted by

the Kinect environment and the available space.

Concerning the angle of the container for settings

with 20° and -20° the internal space of the container

is completely visible for the Kinect. For the actual

loading volume, further variations beyond the ones

used did not lead to new or different results.

Ordering of variable variation. According to

the previously described granularities, each combi-

nation of the different levels has been evaluated,

leading to 100 combinations in total. Each round

consists of 35 measurements, which makes 3500

measurements in total.

Table 1: Independent Variables and Assignments.

Horiz. Distance [m] 2.58, 2.1, 1.62, 1.3

Angle [°] 0, 10, 20, –10, –20

Loading Volume [m³] 0.001, 0.131, 0.262, 0.481, 0.642

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Figure 2: Distance and Deviation. Figure 3: Angle and Deviation. Figure 4: Cargo Volume and Deviation.

5.1.3 Confounding Variables

During the experiment, we observed three main

confounding variables:

 The background and changes in the background

might cause wrong volume estimations. To avoid

this, a static background must be guaranteed dur-

ing the experiment.

 The infrared sensor of the Kinect is sensitive to

daylight. Hence, the experiment location has

been protected from daylight.

 The surface of the parcel-container either reflects

or refracts the infrared light. To remedy this, the

inner container surface has been covered with

cardboard.

5.1.4 Technical Setting and Constraints

The Kinect is located on a tripod at a height of 2.9m,

the inclination angle is -38°. The range of the view-

ing area of the Kinect is 57° horizontal and 43° ver-

tical. The data are processed by a computer equipped

with Intel i7 3770, nVidia GT630, 16GB of memory,

and Windows 7 Professional x64 SP1 as the operat-

ing system. As software, we use Microsoft Visual

Studio 2012, .Net Framework 4.5, Microsoft Kinect

SDK 1.8 including the Developer Toolkit, and XNA

Gamestudio 4.0.

The parcel-container must be located completely

in the viewing area. The size of the parcel-container

is 0.92m * 1.13m * 1.67m (inside) and 0.95m *

1.2m * 1.9m (outside). The open side of the contain-

er is in front of the Kinect such that the inner space

of the container is visible. The parcel-container must

be optimally loaded, i.e., without empty spaces be-

tween parcels. Settings of the Kinect such as resolu-

tion, transformations, and algorithms are fixed.

5.1.5 Expected Effects

When designing the experiment, we identified ex-

pected effects caused by the variation of the inde-

pendent variables, leading us to formulate three

hypotheses (H1 – H3):

 H1/H2: “Variations of the distance/angle have no

influence on the error of measured loading vol-

ume and real loading volume.” The dis-

tance/angle determine the geometric position of

objects. Variations of them are variable factors

that are handled by Kinect algorithms.

 H3: “The larger the real loading volume is the

less are the relative measurement errors.” The

volume is computed as the difference between

the volume of the loaded container and the vol-

ume of the empty container. Due to some general

variations in the scanning, lower volume has

higher impact on deviations.

5.2 Experimental Results

In total, we collected 3.5GB of raw data during ex-

periment execution, covering 3345 out of 3500 pos-

sible measurements. For the analytics, we used IBM

SPSS Statistics v22.0.0.0 x64.

First, we analyzed the influence of the geomet-

rical position of the container on the accuracy

(measurement error).

Figure 2 and Figure 3 show the influence of the

distance and the angle of the container to the Kinect.

Figure 2 indicates that the Kinect delivers a low

error in the 2100mm and 2580mm range. However,

the error increases as the container gets closer to the

Kinect sensor (1620mm and 1300mm). The angle

analysis (Figure 3) shows a similar picture, with the

smallest error being at -10° and 0° and increasing

the further it moves away from the zero point.

We also analyzed the influence of the amount of

payload. Figure 4 shows that the more payload is

used, the less relative measurement error we have,

starting from a very high error for lowest payload up

to a very small error for highest payload.

In general, statistical influence of the independ-

ent variables is highly significant. Table 2 shows a

more detailed analysis. Based on these results, we

Real-timeCargoVolumeRecognitionusingInternet-connected3DScanners

327

constructed a best case scenario where we only

picked the best values for the independent variables

(see Table 3).

Comparing the results of the best case with the

average case, a significant lower standard deviation

can be identified in the best case. This means that

with the choice of optimal variables, it is possible to

heavily reduce the deviation to a minimum. Even if

an optimal position for the container (angle and

distance) cannot be ensured, a low level of deviation

can be observed for high amounts of payload. Com-

paring a 14.90 standard deviation of the average case

for the maximum payload (0.642m³) used and a 7.31

standard deviation in the best case with the same

amount of payload, which compares to a 28.84 std.

deviation in the average case with distance set to

2.58m and 37.59 std. deviation in the average case

with angle set to 0°.

5.3 Discussion

While error values showed a variance of only 15%

in the best case scenario, error and variance in gen-

eral showed a significant movement off the desired

zero baseline. This behavior is mainly caused by the

distance variables to the Kinect and the angle of the

container, leading to a stronger geometrical distor-

tion. The more the container moves off the optimal

orthogonal view, the more SNAP has problems with

recognizing the front surface, leading to faulty vol-

ume measurements. Therefore, the user is advised to

place the container as close to the optimal spot as

possible to ensure accurate measurements.

Also, the amount of payload has a high influence

on deviation. The more payload was used, the lower

the deviation was. This leads to more results that are

accurate the higher the payload is. In practice, users

strive to pack the parcel-containers as full as possi-

ble, thus we can expect relatively ideal situations for

this independent variable.

Considering internal validity, in total, three con-

founding variables have been detected. After the

experiment, a manual review was performed to ex-

hibit evidence of further confounding variables, but

none with noticeable impact on the results have been

observed. Considering external validity, a couple of

adjustments had to be done to ensure a feasible

workload and functionality. This includes using a

fixed set of configuration parameters for the volume

algorithms whose effects have yet to be determined.

Also, the container had to be lined with cardboard,

which is a very unlikely preparation for a practical

use. This is yet an unlikely scenario, so either easy

to apply methods or sensing technologies without

this weakness need to be tested. The Kinect also

needs to be placed in a static environment as chang-

ing of objects within the environment makes a recal-

ibration of the background necessary.

6 QUALITATIVE EVALUATION

We evaluated our approach regarding usefulness,

usability, and adoption by performing a survey study

(questionnaire-based) with domain experts from the

parcel logistics domain during a live demonstration

of our prototype.

For each question of the questionnaire, we of-

fered five choices reflecting the degree of approval,

ranging from “strongly disagree”, “disagree”, “neu-

tral”, and “agree” to “strongly agree”. A partici-

pant’s choice was quantified (1 for strongly agree,

0.75 for agree, 0.5 for neutral, 0.25 for disagree and

0 for strongly disagree), summarized and divided by

the mean value, ending up with a single degree of

approval. The result is summarized in Table 4.

The responses from domain experts indicate the

usefulness of our approach. Especially, knowing the

container volume in operative transport management

(79.2%) was perceived of high importance.

Also, the usability of our volume scanning solu-

tion in practical environments has been confirmed,

e.g., by a high approval of the fact that only a short

training period and only few instructions are neces-

sary. Finally, the analysis of potential adoption indi-

Table 2: Significance analysis.

Independent

Variable

t Significance Sign.

Level

Distance -33.4492941 2.3527E-205 **

Angel 9.877020051 1.26671E-22 **

Cargo Volume -24.4620010 1.8005E-119 **

Table 3: Accuracy (measurement error) for the best case

and the average case.

best case avg. case

std. deviation 7.31 61.41

std. error

(95% confidence interval)

0.61 1.18

mean value -16.68 17.44

median -17.94 -1.87

minimum level

(95% confidence interval)

-17.90 15.12

maximum level

(95% confidence interval)

-15.46 19.76

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cated that there is a high interest to apply our Kinect

approach in general (63.9%).

However, the appropriateness of the provided

functionality for the expert’s individual businesses

received an agreement of only 54.2 %. This may

partially be attributed to the fact that the IT permea-

tion of transport and logistics industry is still quite

low in many areas and thus it may be difficult for

domain individuals to relate to their current state of

practice. Another mentioned aspect was an eventual

vulnerability of the approach to soiled sensors.

Table 4: Results of expert evaluation.

Usefulness

The amount of roll containers is important

for operative transport management.

72.2 %

The used capacity (volume) of each roll

container is important for operative transport

management.

79.2 %

The interaction with the Kinect via mobile

apps is useful.

70.8 %

Usability

Using the automatic volume scan (e.g., with

the Kinect) would only require a short train-

ing period.

76.7 %

Using the automatic volume scan (e.g., with

the Kinect) would require some instructions.

68.3 %

Adoption

I would like to work with the automatic

volume scan (e.g., with the Kinect).

63.9 %

The provided functionality of the automatic

volume scan (e.g., Kinect) is appropriate for

my business.

54.2 %

7 SUMMARY AND CONCLUSION

This paper presented an approach for using Mi-

crosoft Kinect, a low-cost, off-the-shelf 3D scanner,

to measure the cargo volume of a parcel-container in

real-time. To quantitatively assess accuracy of pay-

load volume recognition, we performed controlled

experiments. The evaluation showed a high accuracy

with only 15% relative error under optimal circum-

stances. However, it also revealed some weaknesses

when the geometrical position of the container dif-

fers from the optimal position. Complementing this

quantitative evaluation of our solutions, we per-

formed qualitative evaluations through a survey

study with domain experts, which indicated the

usefulness and applicability of our approach. It thus

has a high potential for practical use.

In the meantime, Kinect for Windows v2 was re-

leased and promises further improvement of our

approach, especially concerning overall accuracy,

noise-reduction. Furthermore, we are evaluating

several other parameters to help improve accuracy

and performance of volume recognition. As an on-

going step, we implement a second algorithm with

Euclidean Cluster Extraction and repeat the experi-

ments for comparison. An interesting further aspect

might be to apply our solution to the Food Supply

Chain, e.g., Big Box

. This would allow scanning

parcel-containers containing objects with the same

size and thus calculate the exact amount of objects.

Finally, we are going to bring our solution into

the cloud, using distributed thin-clients with con-

nected Kinect for Windows v2. This facilitates scal-

ing our solution to extend the visible space and scan

multiple containers as well as outsourcing computa-

tional-intensive algorithms.

ACKNOWLEDGEMENTS

The research leading to these results has received

funding from the European Union's Seventh Frame-

work Program FP7/2007-2013 under grant agree-

ment 604123 (FIspace) and from the EFRE co-

financed operational program NRW.Ziel2 under

grant agreement 005-1010-0012 (LoFIP).

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