Dynamic EKF-based SLAM for autonomous mobileconvergence platforms

Seungwon Oh & Minsoo Hahn & Jinsul Kim

Received: 20 February 2014 /Accepted: 8 May 2014# Springer Science+Business Media New York 2014

Abstract This paper presents a new Simultaneous Localization and Mapping (SLAM)framework for solving the problem of SLAM in dynamic environments. The landmarklocation change causes the error of robot pose estimation and landmark mapping. In thispaper, we propose the Dynamic Extended Kalman Filter (EKF) SLAM based on the indepen-dence of the dynamic landmarks. The proposed framework decomposes the SLAM probleminto a traditional SLAM problem for the static landmarks and individual SLAM problems forthe dynamic landmarks. Therefore, in the dynamic environments, it is able to minimize theerror caused by the dynamic landmarks and reduce the uncertainty in the robot pose and thelandmark locations. In order to validate the proposed approach, we implement an indoormobile robot platform with a Red Green Blue - Depth (RGB-D) sensor and utilize Speeded UpRobust Features (SURF) algorithm to extract appearance-based features. The simulation andexperimental results show the validity and robustness of the Dynamic EKF SLAM in indoorenvironments including the dynamic landmarks.

Keywords SLAM .Mobile robot . Dynamic . EKF. SURF

1 Introduction

Localization is one of the most fundamental problems for mobile robots in unknown environ-ments [25]. Also, mapping technologies are essential for autonomous navigation. Theseproblems, the problem of estimating the robot’s location and the problem of building up a

Multimed Tools ApplDOI 10.1007/s11042-014-2093-0

S. Oh :M. Hahn (*)Korea Advanced Institute of Science and Technology, 335 Gwahak-ro, Yuseong-gu, Daejeon 305-701,South Koreae-mail: mshahn@ee.kaist.ac.kr

S. Ohe-mail: swoh@kaist.ac.kr

J. Kim (*)School of Electronics and Computer Engineering, Chonnam National University, 300 Yongbong-dong,Buk-gu, Gwangju 500-757, South Koreae-mail: jsworld@jnu.ac.kr

map of the environments, are two different tasks in mobile robotics. However, they are deeplyassociated with each other: the localization needs the mapping information, and the accurateposition of the robot is needed to construct the map [22]. That is why, in order to solve theseinterconnections, the concept of simultaneous localization and mapping, also known asSLAM, has attracted extensive interest in the mobile robotics literature. Many stochasticSLAM frameworks have been developed so far.

Some meaningful approaches to the SLAM problem have been introduced, such as theextended Kalman filter (EKF) SLAM [23], the Fast SLAM [16], and the Fast SLAM 2.0 [17].The EKF SLAM estimates the posterior distribution of the robot pose along with the landmarkpositions incrementally. In the last two decades, this approach has been widely used in spite ofthe linearization error that could be accumulated over time. The Fast SLAM is an efficientparticle filter-based algorithm. This method makes use of a significant characteristic of theSLAMproblem: landmark estimates are conditionally independent of the given robot’s path. TheFast SLAM 2.0 is an enhanced and faster version of the Fast SLAM. Recently proposed in [12],the hierarchical RBPF SLAM is a robust SLAM framework in indoor environments, which hassparse and short-range sensors. In order to overcome the sensor limitations, this approach dividedthe entire region into several local maps, which were assumed to be independent of each other.However, these approaches have not been attempted in dynamic environments.

In this paper, we focus on the SLAM problem in dynamic environments. The dynamicenvironment includes moving objects such as wastebaskets, tables, and people. Non-stationaryobjects are detected when the robot revisits an already mapped area where location changes ofthe objects have occurred [28]. Because the error caused by the dynamic objects mayaccumulate over time, SLAM frameworks inevitably fail in the long run [20]. Therefore,mobile robots need to be able to deal with the dynamic landmarks in order to functioneffectively in the real world [29]. To our knowledge, the problem of learning maps in thedynamic environments was considered for the first time by Yamauchi and Beer in 1996 [29].Fox et al. presented a metric variant of Markov localization, as a robust technique to localize amobile robot’s pose in the dynamic environments [7]. They developed a technique for filteringsensor measurements which are corrupted due to the presence of people or other objects notcontained in the robot’s model of the environment. Hähnel et al. used the EM algorithm toincorporate the identification of measurements for the dynamic objects into their localizationand mapping algorithm [9]. However, these researches focused on filtering out the dynamicfeatures to maintain accurate models of the dynamic environments. Murphy introduced amethod to model the dynamic environments using the formalism of factored POMDPs [19].Murphy represented the grid map containing landmark state change information in each cell.However, this approach has no consideration for moving objects.

Modeling dynamic objects recognized as landmarks is an important technique needed tosolve the SLAM problem in the dynamic environments. There have been many researches onthe detection and tracking of moving objects [4, 14, 21, 26, 27]. However, they have focusedon the tracking of moving objects, rather than the location change of the dynamic landmarks.The location of the non-stationary object is changed even when they move out of the robot’sfield of view. Recently, Bibby and Reid proposed a hybrid SLAM framework that combines anoccupancy grid to represent land masses, point features to denote stationary objects, and cubicsplines to show the trajectories of dynamic objects [3]. And, Lee et al. dealt with non-stationary objects, such as wastebaskets, tables, and people [13]. They extracted valid featuresby trimming, division, or removal in the dynamic environments. In 2011, Pirker et al. proposeda visual SLAM method which handles scene dynamics in large environments using theHistogram of Oriented Cameras (HoC) descriptor [20]. However, these researches have onlydealt with the features of the dynamic objects observed by sensors.

Multimed Tools Appl

In order to improve the performance of SLAM frameworks in the dynamic environments,the detected dynamic landmarks should be separated from static landmarks during the wholeprocess of SLAM. In 2005, Wolf and Sukhatme developed an on-line SLAM algorithm in thedynamic environments [28]. It is capable of differentiating dynamic parts from static parts andrepresenting them appropriately on the grid map with an occupancy probability model. Theydemonstrated that the detection of dynamic entities and the SLAM framework are mutuallybeneficial. However, little attention has been paid to the independence of the dynamiclandmarks.

In this paper, we propose a SLAM framework, called Dynamic EKF SLAM, utilizing theconditional independence between landmarks in the dynamic environments. The DynamicEKF SLAM decomposes the SLAM problem into a traditional SLAM problem for the staticlandmarks and individual SLAM problems for the dynamic landmarks. This factored repre-sentation is precise, due to the natural conditional independence in the dynamic environmentswhere the location change of landmarks is intentionally caused by people. Therefore, theDynamic EKF SLAM is able to minimize the error caused by the dynamic landmarks and bemore robust in the dynamic environments.

The rest of this paper is organized as follows. In Sec. 2, we focus on the methodology of theproposed Dynamic EKF SLAM. The simulation results of the method are represented in Sec.3. Sec. 4 summarizes the experimental results in a real environment while conclusions andfuture works are discussed in Sec. 5.

2 Methodology of dynamic EKF SLAM

In this section, we describe a new approach for robust SLAM in dynamic environments. Ingeneral, when a mobile robot explores in unknown environments, the following processes areiterated continuously for autonomous navigation. First, the robot estimates its position andextracts landmarks to build a map of the environments. After that, the landmark locations aredefined by the robot’s pose. Last, the landmark position information corrects the robot’slocation with greater accuracy. However, if landmark locations are changed, a robot cannotknow the altered information, and estimate the exact location of landmarks. And then, therobot localizes itself with the incorrect information. Finally, the robot may lose its way.Because real environments include various dynamic objects, the dynamic landmarks, whoselocation can be changed, are the important issue in the SLAM problem. Therefore, we takeadvantage of the independence of the dynamic landmarks to solve the SLAM problem in thedynamic environments.

2.1 Dynamic landmark factored representation

The dynamic landmark is the landmark whose location may be changed by external causes.The change is not related to information about a robot or other static landmarks. Therefore, inthis paper, we assume that the dynamic landmarks are independent of the static landmarks. Theassumption is based on the natural conditional independences in the dynamic environments.As a result, we can derive a new SLAM approach from the traditional SLAMproblem using this assumption. In other words, the proposed Dynamic EKF SLAMseparates dynamic landmarks from static landmarks. The original SLAM posterior is givenas follows:

p x1:t; l1:mjz1:t; u0:t−1ð Þ ð1Þ

Multimed Tools Appl

where x1:t, l1:m, z1:t, and u0:t−1 are the robot path, the landmarks, measurements, and controls,respectively. For the dynamic environments, we define the landmarks as follows:

l1:m ¼ ls1:ms; ld1:md

n oð2Þ

where ms and md are the numbers of the static landmark s and the dynamic landmark drespectively. Based on the assumption, we separate the dynamic landmarks from the staticlandmarks. Equation (1) is divided into two parts as follows:

p x1:t; l1:mjz1:t; u0:t−1ð Þ¼ p x1:t; l

s1:ms

jz1:t; u0:t−1� �

⋅ p ld1:mdjx1:t; ls1:ms

; z1:t; u0:t−1� �

¼ p x1:t; ls1:ms

jz1:t; u0:t−1� �

⋅ p ld1:mdjx1:t; z1:t

� � ð3Þ

Equation (3) shows that the SLAM framework can deal with the static landmarks and thedynamic landmarks separately with the independence. Also, the dynamic landmarks areindependent of each other. Then the last line of (3) is factorized as follows:

p x1:t; ls1:ms

jz1:t; u0:t−1� �

⋅ p ld1:mdjx1:t; z1:t

� �

¼ p x1:t; ls1:ms

jz1:t; u0:t−1� �

⋅ ∏i¼1

md

p ldi jx1:t; z1:t� � ð4Þ

p x1:t; ls1:ms

jz1:t; u0:t−1� �

of (4) is the same as (1) which means the original SLAM posterior

has to deal with only the static landmarks. Therefore, this clarifies that it is possible to solve theSLAM problem in the dynamic environments by adding the part for the dynamic landmarksirrespective of the traditional SLAM frameworks for the static landmarks. Also, the last line of(4) proves mathematically that the proposed SLAM framework can deal with the dynamiclandmarks individually. According to (4), in this paper, we propose the Dynamic EKF SLAMbased on the EKF SLAM.

Table 1 shows the comparison between the traditional EKF SLAM and the Dynamic EKFSLAM in terms of the dimension of the covariance matrix. Because the traditional EKF SLAMcannot distinguish the dynamic landmarks from the static landmarks, the elements for unnec-essary dynamic landmarks cannot be removed. On the other hand, the Dynamic EKF SLAMcan reduce the computational complexity of the covariance matrix by using individual EKFsfor the dynamic landmarks. Therefore, although the proposed method requires effort tomaintain several SLAM processes simultaneously, it can make up for the weak point of thetraditional EKF SLAM, namely, that the number of landmarks increases due to the dynamiclandmarks.

Table 1 Dimension of covariance matrix

Traditional EKF SLAM Dynamic EKF SLAM

½ N l sþd� �

∗Dim lð Þ þ Dim xð Þ� �

� N l sþd� �

∗Dim lð Þ þ Dim xð Þ� �

�

½ N lsð Þ∗Dim lð Þ þ Dim xð Þ� �

� N lsð Þ∗Dim lð Þ þ Dim xð Þ� ��

&N ld� �

∗½Dim l þ xð Þ � Dim l þ xð Þ�

Multimed Tools Appl

2.2 Feature extraction

Researches on SLAM in indoor environments have produced a lot of progress for mobileservice robots. Instead of GPS sensors, which cannot be used indoors, and expensive lasersensors, other various sensors, such as infrared, ultrasonic, vision, and so on, are utilized torecognize the environments. In addition, there have been many studies on a combination oftwo or more sensors [1, 11].

As vision technology has been developed, the SLAM approaches using a vision sensorhave been proposed [5]. Vision sensors can get rich information about the environment.However, they have difficulty in measuring depth information which is essential for successfulSLAM processes. That is why some researches have suggested SLAM methodologies usingstereo vision sensors [24]. And the new technology, also known as an RGB-D sensor, whichcan acquire a depth image as well as a color image simultaneously, has received a lot ofattention [10, 15]. In other words, it can obtain not only the color image for visual informationbut also the depth image representing the distance of every pixel in the image as shown inFig. 1. Recently, the sensor developed by Microsoft has been widely used in various fields[30]. Several studies have already attempted to adopt it to solve the SLAM problem in indoorenvironments [6, 8].

In this paper, we implement the proposed SLAM framework with the RGB-D sensor inindoor environments. We extract appearance-based features from the color image and estimatethe location information of each feature with the depth image. Also, we utilize the SURFalgorithm, which is very popular in image processing and the analysis area, for featureextraction and matching [2]. It has been already applied to visual SLAM frameworks becauseof its strong advantages of scale- and rotation- invariance and fast speed [18, 31]. However, inorder to utilize the SURF algorithm successfully in SLAM frameworks, the following thingsmust be considered.

First, some procedures have to be considered in order to deal with the depth image. Figure. 1represents the color image and the depth image obtained from the sensor. The green circles of(a) mean the features extracted by the SURF algorithm and the blue ellipses of (b) indicate thepart of strong fluctuations in the depth image. The depth image provides the exact distancevalue with good repeatability but it is less stable compared with the color image. Therefore,additional post-processing is required to utilize the depth value of the RGB-D sensor. In this

Fig. 1 Measurement example of an RGB-D sensor: (a) color image; (b) color mapping of the depth values

Multimed Tools Appl

paper, we apply a smoothing method to the depth image, and then remove the distance data outof the reliable range (50~250 cm).

Next, we have considered some important factors involved with using the SURF algorithmin the SLAM problem. In order to resolve the data association problem, the extracted featureshave to have a unique identifier. In other words, the description information which is used formatching in the SURF algorithm must be able to be discriminated. Therefore, when ameaningful feature is extracted, we select new features having a unique description, aftercomparing it with that of the existing landmarks. Also, we determine the threshold values ofthe SURF properties such as the Hessian value and size in order to choose more robust featuresfor the SLAM. Figure 2 summarizes the feature extraction process.

2.3 Landmark state analysis

Various dynamic factors exist in real environments. However, in this paper, we define twokinds of dynamic landmarks, as shown in Fig. 3: the ingoing landmark and the outgoinglandmark. The ingoing landmark means the landmark has been detected in some other place,not in its first observed location. In other words, after a landmark has moved into a new place,we determine it to be a dynamic landmark, when the landmark is observed to be in the differentlocation. The outgoing landmark is a disappeared landmark in its known location. If thelocation of a landmark was changed, we cannot find the landmark at its original location at themoment. In order to deal with these dynamic landmarks in the proposed SLAM framework,we define four kinds of properties: new, existing, ingoing, and outgoing. The new landmarkmeans a newly observed landmark, and the existing landmark represents a landmark which has

Fig. 2 The feature extraction process

Multimed Tools Appl

been included in the prior map after the robot recognizes the landmarks. These are defined asstatic landmarks. On the other hand, the ingoing landmark and the outgoing landmark areregarded as dynamic landmarks.

First, we suppose the map as follows:

M ¼ Ms;Md� �

Ms ¼ ls1; ls2; :::; l

sms

n o;Md ¼ ld1 ; l

d2 ; :::; l

dmd

n o ð5Þ

Next, the actual measurement information of landmarks is obtained from the robot’s sensorwith the own sensor model as follows:

Zt ¼ l1t ; l2t ; :::; l

kt

� � ð6Þwhere Zt is the actual observation. And, the expected measurement information can becalculated with the robot’s pose, the prior landmark map, and the reliable range determinedin the feature extraction process as follows:

Zet ¼ h X t;Mt−1ð Þ ð7Þ

where Zte is the expected observation. Lastly, the state of landmarks is defined according to the

following conditional expressions:

• lkt ∈Zt and lkt ∉Mt−1→Ms ¼ Ms∪ lkt ¼ New� �

• lkt ∈Zt and lkt ∈Mt−1 and lkt ∈Zet→Ms ¼ Ms∪ lkt ¼ Existing

� �• lkt ∈Zt and lkt ∈Mt−1 and lkt ∉Z

et→Md ¼ Md∪ lkt ¼ Ingoing

� �• lkt ∉Zt and lkt ∈Mt−1 and lkt ∈Z

et→Md ¼ Md∪ lkt ¼ Outgoing

� �

2.4 Individual EKFs for dynamic landmark

The Dynamic EKF SLAM offers a robust method in the dynamic environments with individ-ual EKFs for the independent dynamic landmarks. Figure 4 represents the architecture of theDynamic EKF SLAM, which consists of the traditional EKF SLAM to estimate the staticlandmarks, and individual EKF SLAMs to estimate the dynamic landmarks. The individual

Fig. 3 The diagram of dynamic landmarks during the SLAM process

Multimed Tools Appl

EKF SLAM maintains the posterior distribution over the robot pose and the location of onedynamic landmark. Therefore, because the information of the dynamic landmark cannot havean effect on other landmarks, the Dynamic EKF SLAMmay decrease the influence of the errorpropagation caused by the uncertainty of the dynamic landmark. In addition, it can improve theperformance of the overall SLAM processes with more precise location information.

3 Simulation results

To demonstrate the performance of the Dynamic EKF SLAM framework, we compare it withtwo different ways to deal with the dynamic landmarks using an EKF SLAM simulator inMatlab. The Matlab simulation code is developed by [1]. We modified their code according toour work.

As shown in Fig. 5, the environment determined for the simulation is relatively large about250 m×200 m and 35 landmarks are scattered in the environment. According to predefinedpath information, a mobile robot recognizes the observed landmarks while it takes a turn in theenvironment. And then, after we change the location of some landmarks, it can detect thedynamic landmarks when it turns again. In Fig. 5, the four dynamic landmarks are representedby the plus sign in a rectangle. The landmarks were moved following the each arrow direction.

We conducted a simulation with three different ways of handling the dynamic landmarkswith known data association. The case 1 recognizes a dynamic landmark as a new landmark.The case 2 neglects the dynamic landmarks. The case 3 is the proposed Dynamic EKF SLAM

Fig. 4 The Dynamic EKF SLAM procedure

Multimed Tools Appl

which deals with the dynamic landmarks individually. To validate the effectiveness of theproposed framework, we analyzed the results in three ways such as the robot location error, thelandmark location mapping error, and the variances of the robot and landmarks location.

Figure 6 shows the error of the robot pose estimation according to the path. Initially, allthree cases are the same until the dynamic landmarks appear. According to the way to handle

Fig. 5 Simulation environment

Fig. 6 Robot location error

Multimed Tools Appl

the dynamic landmarks, they have an influence on the error. In this result, the proposedapproach has less error than others.

Figure 7 represents the location mapping errors of the landmarks except the dynamiclandmarks. The first case has rather large errors overall. On the other hand, the proposedframework shows the robust mapping result in the dynamic environment by minimizing theerror propagation caused by the dynamic landmarks.

As shown in Fig. 8, the Dynamic EKF SLAM has rather less uncertainty than others. Thediagonal elements of the covariance matrix represent the location uncertainties of the robot andthe landmarks. Relatively high points of the case 1 represent the dynamic landmarks. And thecase 2 has a little higher variance than the first case because ignoring dynamic landmarksdecreases the chance of the measurement update. On the other hand, the case 1 should considermore landmarks than others.

In addition, we analyzed the difference according to the change of the number of the dynamiclandmarks. As shown in Fig. 9 and Fig. 10, the more the number of the dynamic landmarkincreases, the more the Dynamic EKF SLAM improves the performance. When the number ofdynamic landmarks was four, the proposed framework resulted in a 7.85 % decrease in theaverage robot location error and a 27.85 % decrease in the average landmark location error.

As shown in these results, we can confirm that the Dynamic EKF SLAM is more robust in thedynamic environments by using individual EKFs for the dynamic landmarks. It prevented theaddition of unnecessary landmarks and decreased the influence of the error propagation caused bythe uncertainty of the dynamic landmarks. In addition, it leaded to the performance improvementof the SLAM framework by increasing the location accuracy of the robot and the landmarks.

4 Experiments

4.1 Indoor mobile robot platform

We developed an indoor mobile robot platform with an RGB-D sensor to evaluate theproposed SLAM framework in real environments, as shown in Fig. 11. It has two motors:

Fig. 7 Landmark location error

Multimed Tools Appl

one for a left wheel and another for a right wheel. Therefore, it can move in four kinds of waysuch as forward, backward, left rotation, and right rotation. The tablet PC built into the robotmeasures environmental information with the RGB-D sensor and controls the motions of therobot.

4.2 Visual SLAM procedure

Figure 12 summarizes our proposed visual SLAM procedure. As shown in the figure, theSLAM framework recognizes a robot’s pose and creates a map of an indoor environment by

Fig. 8 Variance of the robot and the landmarks

Fig. 9 Robot location error ac-cording to the number ofdynamic landmarks

Multimed Tools Appl

Fig. 10 Landmark location erroraccording to the number ofdynamic landmarks

Fig. 11 Indoor mobile robot platform

Multimed Tools Appl

performing repetitive procedures. The velocity-based motion model estimates the new positionof the robot, as follows:

X t ¼xtytθt

24

35 ¼

xt−1 þ Δst;r þΔst;l2

cos θt−1 þ Δst;r−Δst;l2b

�

yt−1 þΔst;r þΔst;l

2sin θt −1 þ Δst;r−Δst;l

2b

�

θt−1 þ Δst;r−Δst;lb

26666664

37777775

ð8Þ

whereΔst,r andΔst,l are the moving distance of the right and left wheels, respectively, and b isthe distance between the both wheels.

Fig. 12 Procedure diagram of an RGB-D sensor-based visual SLAM using SURF

Multimed Tools Appl

The pose of the robot at time t is represented as Xt. It comprises its location and orientationrelative to the global coordinate frame. x, y, and θ represent the x-coordinate, the y-coordinate,and the orientation of the robot, respectively.

The measurement, Zt, is the collection of the features detected by the SURF in the colorimage of the RGB-D sensor. It comprises its point, size, and description vector. The locationrelative to the global coordinate frame can be estimated with the depth information, d, asshown in Fig. 13. θv and θh are the vertical and the horizontal angles between the robot and thefeatures, respectively and can be calculated with the point of the feature in the color image. Byusing equation (9), the location of the feature can be estimated.

zk ¼zkxzkyzkz

264

375 ¼

xx þ dxy cos θþ θhð Þxy þ dxy sin θþ θhð Þxz þ d sin θvð Þ

24

35 ð9Þ

And then, we handle the data association problem between the features and the knownlandmarks with the description vector of the SURF. According to the matching results, thefeatures are categorized into two classes: new features to become new landmarks and featuresthat match existing landmarks. The location information of the robot and the landmarks isupdated by using the information of the matched feature.

4.3 Experimental setup

The Dynamic EKF SLAM focuses on the dynamic environment, which is that the location oflandmarks can be changed intentionally. In real environments such as home and office, peopleoften rearrange some objects situated in their space. Based on these conditions, we configurethe experimental environment in order to verify the feasibility of the proposed approach asshown in Fig. 14 a. After the robot recognizes some objects as landmarks in the environment,one of them is moved into a different place out of the robot’s field of view. The location changecannot help affecting the posterior distribution during the SLAM process. Therefore, wecompare the difference between the EKF SLAM and the Dynamic EKF SLAM.

4.4 Experimental results

As shown in Fig. 14b, because the EKF SLAM does not consider the dynamic landmarks, itcannot recognize that the object having the feature recognized as a landmark has disappeared.In addition, when the object reappears in a different place, the difference between the previous

Fig. 13 Feature locationestimation

Multimed Tools Appl

location and the new location is corrected gradually by the SLAM processes with the SURFdescription-based data association. During this update period, the EKF SLAM raises thelocation error and rather larger uncertainty in its overall information, because of the errorcaused by the difference. However, as shown in Fig. 14c, the Dynamic EKF SLAM deter-mines the location-changed landmark as a dynamic landmark with the proposed landmark stateanalysis approach, and handles it with individual EKF SLAM based on the new locationinformation. When the landmark disappears, its status changes from the Existing to theOutgoing and it is not displayed on the map. Also, when the landmark appears in the differentplace, the status is defined as the Ingoing and it is processed by a new independent EKF.Although the uncertainty of the dynamic landmark is too large, it is rather precise informationbecause the location is changed. Therefore, the dynamic landmark does not have a badinfluence on the SLAM framework and the proposed approach can maintain the correct maprepresentation at every moment. In the EKF SLAM, the average location error of the robot andthe landmarks caused by the dynamic landmark was about 0.3 m.

As shown in Fig. 14, we can confirm that the Dynamic EKF SLAM is more robust in thedynamic environments by using individual EKFs for the dynamic landmarks. As a result, itimproved the accuracy of the posterior distribution over the location of the robot and the landmarks.

5 Conclusions

In this paper, we have suggested a new SLAM method that considers the location change oflandmarks. First, we acquire the color image and the depth image from the RGB-D sensor inindoor environments. And then, we detect the dynamic landmarks through the landmark state

Fig. 14 Experiment results

Multimed Tools Appl

analysis process with data association based on the SURF descriptions. The Dynamic EKFSLAM based on the independence of the dynamic landmarks decomposes the SLAM probleminto a traditional EKF for the static landmarks and individual EKFs for the dynamic land-marks. The proposed approach deals with the independent dynamic landmarks separately.

The simulation results showed the validity and robustness of the proposed algorithm. Inaddition, the experimental results described the difference between the original EKF SLAMand the Dynamic EKF SLAM in the indoor environment including the dynamic landmarks.Although it requires effort to handle several SLAM processes simultaneously, it improved theoverall SLAM performance by using individual EKFs for the dynamic landmarks.

However, we should confirm the long-term operation of the proposed framework in morecomplex environments. Also we need to consider other SLAM frameworks for the staticlandmarks. In future work, we will focus on the comparison with other SLAM algorithms andconsideration of other dynamic factors.

Acknowledgments This research is supported by Ministry of Culture, Sports and Tourism (MCST) and KoreaCreative content Agency (KOCCA) in the Culture Technology (CT) Research & Development Program and alsosupported partially by the MSIP (Ministry of Science, ICT & Future Planning), Korea, under the ITRC(Information Technology Research Center) support program (NIPA-2013-H0301-13-3005) supervised by theNIPA (National IT Industry Promotion Agency).

References

1. Bailey T, Neito J, Guivant J, Stevens M, Nebot E (2006) Consistency of the EKF-SLAM Algorithm. In:IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 3562–3568

2. Bay H, Ess A, Tuytelaars T, Gool LJV (2008) Speeded-up robust features (SURF). Comp Vision ImageUnderst (CVIU) 110(3):346–359

3. Bibby C, Reid I (2010) A hybrid SLAM representation for dynamic marine environments. In: IEEEInternational Conference on Robotics and Automation (ICRA), pp 257–264

4. Chung SY, Huang HP (2008) Simultaneous topological map prediction and moving object trajectoryprediction in unknown environments. In: IEEE/RSJ International Conference on Intelligent Robots andSystems (IROS), pp 1594–1599

5. Davison AJ, Reid ID, Molton ND, Stasse O (2007) MonoSLAM: real-time single camera SLAM. IEEETransactions on Pattern Analysis and Machine Intelligence 29:1052–1067

6. Endres F, Hess J, Engelhard N, Sturm J, Cremers D, Burgard W (2012) An evaluation of the RGB-D SLAMsystem. In: IEEE International Conference on Robotics and Automation (ICRA), pp 1691–1696

7. Fox D, Burgard W, Thrun S (1999) Markov localization for mobile robots in dynamic environments. J ArtifIntell Res 11:391–427

8. Ganganath N, Leung H (2012) Mobile robot localization using odometry and kinect sensor. In: IEEEInternational Conference on Emerging Signal Processing Applications (ESPA), pp 91–94

9. Hähnel D, Triebel R, Burgard W, Thrun S (2003) Map building with mobile robots in dynamic environ-ments. In: IEEE International Conference on Robotics and Automation (ICRA) 2:1557–1563

10. Hochdorfer S, Schlegel C (2010) 6 DoF SLAM using a ToF camera: The challenge of a con-tinuouslygrowing number of landmarks. In: IEEE/RSJ International Conference on Intel-ligent Robots and Systems(IROS), pp 3981–3986

11. Howarth B, Katupitiya J, Guivant J, Szwec A (2010) Novel robotic 3D surface mapping using range and visionfusion. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 1539–1544

12. Lee T, Lee S, Oh, S (2011) A hierarchical RBPF SLAM for mobile robot coverage in indoor environments.In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 841–846

13. Lee S, Park B, Lim J, Cho D (2010) Feature map management for mobile robots in dy-namic environments.Robotica 28:97–106

14. Lin K, Wang C (2010) Stereo-based simultaneous localization, mapping and moving object tracking. In:IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 3975–3980

Multimed Tools Appl

15. May S, Werner B, Surmann H, Pervolz K (2006) 3D time-of-flight cameras for mobile robot-ics. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 790–795

16. Montemerlo M, Thrun S, Koller D, Wegbreit B (2002) FastSLAM: a factored solution to the simultaneouslocalization and mapping problem. In: Proc. AAAI

17. Montemerlo M, Thrun S, Koller D, Wegbreit B (2003) FastSLAM 2.0: an improved particle filteringalgorithm for simultaneous localization and mapping that provably converges. In: International JointConference on Artificial Intelligence, pp 1151–1156

18. Murillo AC, Guerrero JJ, Sagues C (2007) SURF features for efficient robot localization with omnidirec-tional images. In: IEEE International Conference on Robotics and Automation (ICRA), pp3901-3907

19. Murphy KP (1999) Bayesian map learning in dynamic environments. Advances In Neural InformationProcessing Systems

20. Pirker K, Ruther M, Bischof H (2011) CD SLAM - continuous localization and mapping in a dynamic world.In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 3990–3997

21. Rogers JG, Trevor AJB, Nieto-Granda C, Christensen HI (2010) SLAM with Expec-tation Maximization formoveable object tracking. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS),pp 2077–2082

22. Siegwart R, Nourbakhsh IR (2004) Introduction to AutonomousMobile Robots, MIT Press, USA, pp 250–25623. Smith R, Self M, Cheeseman, P (1990) Estimating uncertain spatial relationships in robotics. Autonomous

robot vehicles, pp 167–19324. Takezawa S, Herath DC, Dissanayake G (2004) SLAM in indoor environments with stereo vision. In: IEEE/

RSJ International Conference on Intelligent Robots and Systems (IROS), pp 1866–187125. Thrun S, Burgard W, Fox D (2005) Probabilistic robotics, MIT Press, USA, pp 191–23626. Wang C, Thorpe C, Thrun S (2003) Online simultaneous localization and mapping with detection and

tracking of moving objects: theory and results from a ground vehicle in crowded urban areas. IEEEInternational Conference on Robotics and Automation (ICRA) 1:842–849

27. Wangsiripitak S, Murray DW (2009) Avoiding moving outliers in visual SLAM by track-ing moving objects.In: IEEE International Conference on Robotics and Automation (ICRA), pp 375–380

28. Wolf DF, Sukhatme GS (2005) Mobile robot simultaneous localization and mapping in dynamic environ-ments. Auton Robot 19:53–65

29. Yamauchi B, Beer R (1996) Spatial learning for navigation in dynamic environments. IEEE Transactions onSystems, Man, and Cybernetics, Part B: Cybernetics 26:496–505

30. Zhang Z (2012) Microsoft kinect sensor and its effect. IEEE MultiMedia 19(2):4–1031. Zhang Z, Huang Y, Li C, Kang Y (2008) Monocular vision simultaneous localization and mapping using

SURF. In: World Congress on Intelligent Control and Automation, pp 1651–1656

Seungwon Oh received the B.S. degree in information and communications engineering from SungkyunkwanUniversity in 2007, and the M.S. degree in information and communications engineering from Korea AdvancedInstitute of Science and Technology (KAIST) in 2009. He is currently a Ph.D. candidate in the Information andCommunications Engineering Department, KAIST.

His research interests include intelligent service robots, human-computer interaction, multimedia communi-cation, and digital media.

Multimed Tools Appl

Minsoo Hahn received his B.S. and the M.S. degrees in electrical engineering from Seoul National University,Seoul, South Korea, in 1979 and 1981, respectively, and the Ph.D. degree in electrical and electronicsengineering from University of Florida, USA, in 1989. From 1982 to 1985, he was with the Korea ResearchInstitute of Standards and Science, Deajeon, South Korea. From 1990 to 1997, and he was with the Electronicsand Telecommunications Research Institute, Deajeon, South Korea. From 1998 to 2008, he was a facultymember of the School of Engineering, ICU. From 2009, he has been a full professor of the Department ofInformation and Communications engineering, and a director of the Digital Media Laboratory, Korea AdvancedInstitute of Science and Technology. His research interests include speech and audio coding, noise reduction,sound source localization, intelligent service robots, multimedia communication, and digital media.

Jinsul Kim received the B.S. degree in computer science from University of Utah, Salt Lake City, Utah, USA, in2001, and the M.S. and Ph.D degrees in digital media engineering, department of information and communica-tions from Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea, in 2005 and2008.

He worked as a researcher in IPTV Infrastructure Technology Research Laboratory, Broadcasting/Telecommunications Convergence Research Division, Electronics and Telecommunications Research Institute(ETRI), Daejeon, Korea from 2005 to 2008. He worked as a professor in Korea Nazarene University, Chon-an,Korea from 2009 to 2011. Currently, he is a professor in Chonnam National University, Gwangju, Korea. He hasbeen invited reviewer for IEEE Trans. Multimedia since 2008. He has been invited for TPC(Technical ProgramCommittee), IWITMA2009/2010, and PC(Program Chair), ICCCT2011 His research interests include QoS/QoE,Measurement/Management, IPTV, Mobile IPTV, Smart/Social TV, Multimedia Communication and DigitalMedia Arts.

Multimed Tools Appl