Mapping Indoor Environments Based on Human Activity

Slawomir Grzonka Frederic Dijoux Andreas Karwath Wolfram Burgard

Abstract— We present a novel approach to build approximatemaps of structured environments utilizing human motion andactivity. Our approach uses data recorded with a data suitwhich is equipped with several IMUs to detect movements of aperson and door opening and closing events. In our approachwe interpret the movements as motion constraints and doorhandling events as landmark detections in a graph-based SLAMframework. As we cannot distinguish between individual doors,we employ a multi-hypothesis approach on top of the SLAMsystem to deal with the high data-association uncertainty. As aresult, our approach is able to accurately and robustly recoverthe trajectory of the person. We additionally take advantage ofthe fact that people traverse free space and that doors separaterooms to recover the geometric structure of the environmentafter the graph optimization. We evaluate our approach inseveral experiments carried out with different users and inenvironments of different types.

I. I NTRODUCTION

The problem of localizing and tracking people has recentlyreceived substantial attention in the robotics community asknowledge about the current position of a person and his orher activity allows a mobile robot to improve its servicesto its users. For example, the robot can better anticipatefuture actions of the person. Additionally, knowledge aboutthe environment and the location of people can greatlysupport search and rescue missions in emergency situations.Consider, for example, firefighters in a building enclosedby smoke and fire. If a map of the environment can beconstructed while the firefighters are within the building, anoperator or automated system can re-route the people to theexit in case of an emergency. Alternatively, one could usethe map of the environment to more intelligently coordinatethe actions of the rescue workers to more effectively searchthe environment for potential victims and at the same timereduce the time the rescue workers are exposed to potentialthreats.

In this paper, we consider the problem of simultaneouslyestimating the trajectory of a person walking through anindoor environment and the map of the environment. Ourapproach utilizes the movements of a person as well asdoor handling activities to reconstruct the trajectory of theperson as well as the map of the environment. The estimationis carried out based on data recorded with an Xsens datasuit, which is equipped with 17 inertial measurement units(IMUs), worn by a human. This data suit records full bodypostures of the person and in this way allows for a predictionof the motion as well as for the identification of unique and

Department of Computer Science, University of Freiburg, D-79110Freiburg, Germany

Fig. 1. Human activities like opening a door (top left and right) are usedto detect loop closures. Given this information, the pure odometry (a) canbe corrected (b) leading to a consistent trajectory. Based on this correctedodometry and the knowledge about the location of individual doors, anapproximate map of the environment can be calculated (c). Here,lightblue/gray squares indicate the location of individual doors. A laser basedmap of the same environment is shown in (d) for comparison.

location-based activities such as door handling events. Thetwo top images of Figure 1 depict a person wearing the Xsensdata suit (left) and the posture estimated by the softwaredelivered with it (right).

We present an approach that is able to learn the motionscarried out by a human during handling a door with eitherthe left or the right hand. This learned motion is then usedto detect door handling events and at the same time toestimate the location of doors while the human is walkingthrough the environment. We then apply a graph-basedSLAM approach that uses the odometry estimated by theIMUs and the landmarks corresponding to the door handlingevents to estimate the true path of the person. To deal with thecorresponding data association uncertainty in the landmarkdetermination, we apply a multi-hypothesis scheme. Aftercalculating the path of the person, we utilize the pose of theestimated doors to calculate an approximate two-dimensionalmap of the environment.

The paper is structured as follows. After discussing relatedwork we briefly describe the algorithms used for learningand detecting the motion for handling a door. Section IV re-

views the multi-hypothesis tracker for sensors providing onlypositive feedback, especially the expressions to calculate thehypothesis probabilities. Section V describes how we detectpotential loop closure candidates and our overall system. InSection VI we present our experimental results based on realdata recorded by different people walking inside and outsideof various buildings.

II. RELATED WORK

The problem of human indoor navigation and localizationhas recently become an active research field [6], [12],[7], [2]. A number of different sensors are employed aswell as different kind of localization techniques were used.One of the first works in this area is the one by Leeand Mase [6], where wearable accelerometers and othersensors, like a digital compass and a velocity sensor, wereemployed to recognize when humans perform specific ac-tivities and switch between indoor locations. They integratethe accelerometer data over time to localize humans withina known environment, using higher level descriptors likestanding - 2 steps north - 40 steps east -etc. The fieldof indoor navigation and localization is therefore closelyrelated to activity recognition using accelerometer data.[1],[10] present approaches to predict certain low level activitieslike walking , standing, running, sit-ups, and othersusingpurely extracted features from raw accelerometer data anda variety of different learning algorithms. However, they donot employ this information for indoor positioning. In [12],the authors utilize an accelerometer together with an infraredproximity sensor mounted on a pair of headphones to detectwhen a human is passing through a doorway. In this work, theauthors are able to construct topological maps, where roomsare represented by single nodes and edges represent the pathin steps between doorways. For building these maps and fordetecting loop closures, the human user has to indicate bygesture which door was passed, i.e. giving each door a uniqueidentifier via the infrared proximity sensor. Within this map,the approach also allows for localization based on Bayesianfiltering. HeadSLAM by Cinaz and Kenn [2] employs a laserscanner together with an IMU mounted on a helmet. Theyuse the IMU sensor to be able to project the laser scans intoa horizontal plane in a global coordinate system and employa modified GMapping [5] implementation, by incorporatinga simple motion model with either a fixed speed assumptionfor walking or no speed while standing.

III. M OTION TEMPLATES

Since beside the current pose of the body segments nofurther information is available, we need to track thosemotions in order to detect activities likeopening/closing adoor. Without this additional information we can not detectloop closures and we can only generate an approximate mapbased on the current odometry. This, however, would leadto an inconsistent map due to small errors accumulatingover time, as shown in Figure 1(a). We therefore proposeto detect the motion used for handling a door based onmotion templates (MT) as proposed by Muller et al. [8].

Fig. 2. A synthetic example: Given two examples (a) and (b) of the samemotion walking. The featuresfl, fr are 1 (white) iff the left/right foot isin front of the body and 0 otherwise. The resulting merged template isdepicted in (c). Here, gray areas indicate the value0.5, meaningdon’t care.Intuitively, the matrix can be interpreted through the following sections:feetparallel, right foot in front, feet parallel, left foot in front, feet parallel..

The key idea of the work by Muller et al. is to use simpleboolean features likeright hand is above headand createmore expressive features (motion templates) by conjunctionof the simple ones. Givenf of those features and a motionsequence of lengthK this leads to a matrix of sizef ×K.Note that each entry of this matrix is either 1 or 0 indicatingthis feature being active or not at the specific time andthat the sequence lengthK can in general be differentfor each motion sequence. Consider for example the twofeaturesfl, fr with fl indicating the left foot being in frontof the body andfr active if the right foot is in frontof the body. Given this set of features, a typical walkingtemplate for two different sequences of the same lengthcould look like Figure 2(a) and (b). The learned templategiven these two examples is depicted in Figure 2(c). Here,black and white correspond to 0 and 1 respectively. The gray-shaded boxes account for the fact0.5 meaningdon’t care.More formally, let C denote a motion class consisting ofs templates MT1 . . .MTs indicating the same motion. Eachtemplate MTj is described by a matrix of sizef ×Kj anda weight vectorαj with αj(k) being the weight of thek-thcolumn. Initially, we set all weights to1 indicating each timestep to be equally important. The learning of a class referencetemplate CT is done performing the following steps. First,we select a template MTi from the classC and use it as areferenceR. We then compute an optimal alignment of thisreference template to all other templates from the same classutilizing Dynamic Time Warping (DTW) [9]. We alter thecurrent motion template MTj (which was aligned toR) in thefollowing way. Givenn columns of the reference templateR, namely R(k), . . . , R(k + n − 1), are matched to onecolumn of the current template MTj(l), this column MTj(l)is duplicatedn times having the new weightα(l)/n. In caseone columnR(k) is matched tom columns of MTj , namelyMTj(l), . . . ,MTj(l+m−1), thesem columns are averaged(multiplied by their weights) to one with the new weightbeing the sum of these columns’ weights. This process iscalledwarping. Now, that each MTj has the same length asthe referenceR, we calculate in the second step a weightedaverage template from all templates based on the columnsand their associated weights. Consequently, this step is calledaveraging. In the third step, calledunwarping we stretchand contract the resulting template so that all but eventuallythe last column have a weight of1. In this case, given theweight of a columnα(k) < 1, we merge this column with

subsequent ones until the weight is1. In case the weightof a columnα(k) > 1, we duplicate this column into twocolumns, one with weight1 and the second with weightα(k)− 1. The process is then repeated for the next column.

We repeat the whole procedure of warping, averaging,and unwarping for all templates MTi where every templateonce is the reference templateR. After this step, we replaceall templates by the outcome of the procedure when thistemplate was the referenceR. Since the selection of thereferenceR induces a bias on the learned template, weiterate the whole procedure until no major change betweenthe different templates exists. The outcome of this algorithmis the class template CT. Note that due to the stepswarping,averagingandunwarpingthe values of the resulting matrixare now∈ [0, 1] instead of∈ 0, 1. The final step thereforeconsists of changing each entry of CT into either 0, 1 or0.5,with 0.5 indicating the flagdon’t care. We achieve this byselecting a thresholdγ and changing each entry to0, givenits current value< γ. The value is altered to1 if it was> (1 − γ) and set to0.5 otherwise. In all our experimentswe setγ to 0.1. An example of such a template is depictedin Figure 2(c).

Now, given the learned class template CT and a newmotion sequence, we can calculate a similarity betweenboth. Therefore, we align the motion template of the actualsequence to CT via DTW. We obtain a score between bothtemplates by dividing the amount of mismatches by thenumber of used cells (i.e., the cells being either 0 or 1).Given this score is below a thresholdτ , the actual motionsequence is said to belong to the motion class CT.

Since we are only interested in the motion used forhandling a door with either the left or the right hand we usefeatures based on the pose and velocity of the hands only.Intuitively, we use a set of features describing whether thehand is at the level of the door handle, whether it is raising,hold still or lowered, and finally whether the hand is movingtowards the body or away from it. We learned the templatefor the activity handling a door, which consists of the foursubclassesopen left, close left, open right, close right, using10 examples from a training data set for each subclass. Basedon a second validation data set, we selected the thresholdτ = 0.25 for detecting the motion. Using this threshold, wedid not encounterfalse positiveson the validation data set.Although the features used for detecting a door are quitesimple, we can reliably detect the timestamp when the doorhandle was touched within1.5 seconds of the true timestamp(i.e., manually labeled ground truth). Therefore, we can nowuse the pose of the hand as an approximation of the locationof the door. Given this algorithm we are able to detect whenwe toucheda door, but not which one. We therefore have totake care about possible data associations, which is describedin the next section.

IV. M ULTI HYPOTHESISTRACKING

In this section we review the Multi Hypothesis Tracker(MHT) as described by Reid [11] for sensors providing onlypositive feedback. In the original paper by Reid, this type of

sensor is called a ”type 2” sensor. There, any measurementcan be either detected (assigned to an existing track), markedas a false alarm or as a new track. Since in our particularcase the tracks are static doors, we will call them doorsin the remainder of this section, rather than tracks. Asdescribed in Section III we select a threshold for detectionin such a way, that we do not have to model false positives.Therefore, a measurement can only be interpreted asdetected(when matched to an existing door) or as anew door. Inorder to derive the probabilities of individual measurementassignments we briefly reconsider the formulation of theMulti Hypothesis Tracker for type 2 sensors.Let Ωk

j be thej−th hypothesis at timek andΩk−1p(j) the parent

hypothesis from whichΩkj was derived. Let furtherΨj(k)

denote an assignment, that based on the parent hypothesisΩk−1

p(j) and the current measurementzk gives rise toΩkj . The

assignment setΨj(k) associates the current measurementeither to an existing door or a new door. Given the probabilityof an assignment and the probability of the parent hypothesisΩk−1

p(k), we can calculate the probability of each child thathas been created throughΨj(k). This calculation is donerecursively [11]:

p(Ωkj |zk) = p(Ψj(k),Ω

k−1p(j) |zk)

Bayes+=

Markovη p(zk|Ψj(k),Ω

k−1p(j))p(Ψj(k)|Ω

k−1p(j)) ·

p(Ωk−1p(j)). (1)

The rightmost term on the right-hand side is the recursiveterm, i.e., the probability of its parent. Factorη is a nor-malizer. The leftmost term on the right-hand side after thenormalizerη is the measurement likelihood. We assume thata measurementzk associated with a doorj has a Gaussianpdf centered around the measurement predictionzjk withinnovation covariance matrixSj

k, N (zk) := N (zk ; zjk,S

jk).

Here, the innovation covariance matrix is the uncertaintyof the door w.r.t. the current trajectory and is described inSection V. We further assume the pdf of a measurementbelonging to a new door to be uniform in the observationvolumeV with probability V −1. Hence, we have

p(zk|Ψj(k),Ωk−1p(j)) = N (zk)

δV δ−1 , (2)

with δ being 1 if and only if the measurement has beenassociated with an existing door, 0 otherwise. The centralterm on the right-hand side of Equation (1) is the probabilityof an assignment set,p(Ψj(k)|Ω

k−1p(j)), which is composed of

the following two terms: the probability of detectionpdetkj

and the probability of a new door. In our case the probabilityof detection is equal to choosing one of the current candidatedoors, i.e., all doors within an uncertainty ellipsoid. There-fore, pdetk

j:= NC(x1:k,Ω

k−1p(j))

−1, with NC(x1:k,Ωk−1p(j))

being the number of door candidates, assuming the trajectoryx1:k within the worldΩk−1

p(j) . Assuming the number of newdoors following a Poisson distribution with expected numberof doorsλnewV in the observation VolumeV we obtain

p(Ψj(k)|Ωk−1p(j)) = pδ

detkj

· µ(1− δ;λnewV ) (3)

Fig. 3. A snapshot from our experiment which is described in detail in Section V. (a) The human re-enters the building through door A0. Based on theMHT decisionnew doorandmatch with A0different hypothesis are generated (b) and (c). The probabilities of the hypothesis are depicted in (d).

whereµ(n;λV ) :=(λV )n exp(λV )

n!is the Poisson distri-

bution for n events given the average rate of events isλV .Therefore, Equation (1) can be reformulated into

p(Ωkj |zk) = p(Ψj(k),Ω

k−1p(j) |zk)

Bayes+=

Markovη p(zk|Ψj(k),Ω

k−1p(j))p(Ψj(k)|Ω

k−1p(j)) ·

p(Ωk−1p(j))

= ηN (zk)δV δ−1pδ

detkj

(λnewV )1−δ ·

exp(λnewV )(1− δ)!−1p(Ωk−1p(j)). (4)

Observing that(1− δ)! is always 1 (sinceδ is ∈ 0, 1) andnoting thatexp(λnewV ) can be taken into the normalizerη,we can finally rewrite Equation (4) into

p(Ωkj |zk) = η

(

N (zk) pdetkj

)δ

· λ1−δnew

· p(Ωk−1p(j)). (5)

Up to now, we can reliably detect doors and calculate theprobability of a data association. In the next section weaddress the remaining questions during our simultaneouslocalization and mapping procedure, namely the detection ofpossible door candidates (i.e., loop closures), the calculationof the innovation covariance and the algorithms which wereutilized in order to correct the trajectory.

V. SIMULTANEOUS LOCALIZATION AND MAPPING

We address the simultaneous localization and mappingproblem by its graph based formulation. A node in thegraph represents a 3DoF pose of the human pose (i.e., thecenter of the hip) or the 3DoF pose of a door and anedge between two nodes models a spatial constraint betweenthem. These spatial constraints arise either from incrementalodometry or by detecting a previously observed door (i.e.,by opening/closing it). In our case, the edges are labeledwith the relative motion between two nodes. To computethe spatial configuration of the nodes which best satisfy theconstraints encoded in the edges of the graph, we utilizestochastic gradient descent optimization [4]. Performingthisoptimization whenever a door has been detected allows usto reduce the uncertainty in the current pose estimate.

Since we are only able to detect the fact that thereis a door, we have to track different possibilities of dataassociation, namely whether the current detected door is oneof the already mapped doors, or whether the door has not

been perceived before. We therefore utilize multi hypothesistracking as described in the previous section for all possibleoutcomes. To detect a potential loop closure (i.e., recognizea previously seen door), we identify all former doors whichare within the uncertainty ellipsoid of the current pose bya Dijkstra projection of the node covariances starting fromthe current position. The innovation covariance is directlyused for calculating the likelihood of the door as describedin Equation (5). All doors being within the 99.9% confidenceregion of the current pose are considered as potential loopclosure candidates, and together with the possibility ofthe current detected door being anew door, give raise ton+ 1 different outcomes, given the number of loop closurecandidates isn. For each of these association possibilitieswe create a separate graph, encode the selected constraintand optimize it. The multi hypothesis tree therefore growsexponentially in time and pruning of this tree is mandatoryin order to keep computational costs reasonable. In our case,we utilize N-scan-backpruning as proposed by Cox andHingorani [3], which works as follows: The N-scan-backalgorithm considers an ancestor hypothesis at timek − Nand looks ahead in time to all its children at the currenttime k (the leaf nodes). The probabilities of the childrenare summed up and propagated to the parent node at timek − N . Given the probabilities of the possible outcomes attime k −N , the branch with the highest probability at timek is maintained whereas all others are discarded. Since inour case, a step in the MHT only arises when a door hasbeen detected, this is identical to localizeN steps aheadin time (at door level). An example of this approach isvisualized in Figure 3. This example shows a snapshot ofone experiment which is described in detail in Section VI.At the specific timet, the human walked around the builingleaving at the top exit and entered the building through themain entry labeled A0 in 3(a). Starting from the posez,where the current door was detected, the uncertainty of thepose was back-propagated utilizing Dijkstra expansion. Sincewe used the same uncertainty forx and y, the resultingellipsoid is a circle. Note that due to the back-propagationofthe uncertainty the current pose is in the uncertainty regionof the door A0. For better visibility, only the doors beingconsidered as candidates are shown with their uncertaintyregions. Therefore, only two data associations are possiblein this case, namely matching the current door with A0,

Fig. 4. The first experiment contains a trajectory of about 1.6km. The raw odometry is depicted in (a), whereas the most likely trajectory based onthe detected doors is visualized in (b). Based on the corrected trajectory and the knowledge about doors, a topological map of the environment can begenerated. A skeleton of the environment is generated afterwards by enlarging the free space around the trajectory up to amaximum (c). A close-up viewof the internal part is also shown in (d). A laser map of the same enviornment is depcited in (e). A close up view of the trajectory is also shown in Figure 1.

which in this case is the correct association, or marking itas a new door. Calculating the posterior probability of eachassociation leads top = 0.597 for the casenew doorandp = 0.403 for the correct association. A maximum likelihoodapproach therefore selects the wrong association. However,as the human enters the building and opens another door,given the previous association, different possible outcomesare possible. Figure 3(b) depicts the situation for the casethat the previous decision wasnew door and Figure 3(c)shows the situation for the decisionmatch with A0. Giventhis sequence of doors, the full posterior of the branchmatchwith A0at timet sums up to0.6317 while the probability forthe branch fornew doorsum up to0.3683 (see Figure 3(d)).Here, a N-scan-back of 2 would be sufficient to keep trackof the correct data association, since the MHT would decideto keepmatch with A0at timet and discard the other branch.

The output of this approach can be used to generate anapproximate map of the environment. Assuming that doorsseparate rooms, we can cut the trajectory based on thelocations of individual doors. Each segment now containsall points belonging to one room only. Given the orientationof a door we can merge subsequent segments which are bothconnected to the same door and on the same side. In order toseek for walls, we can furthermore enlarge the trajectory untilit touches a trajectory belonging to another room or up to athresholdd, which was set to2.5m in all our experiments.An outcome of this process is shown in Figure 4(c).

VI. EXPERIMENTS

We evaluated the approach described above on differ-ent data sets utilizing the motion of two humans in-cluding walking inside and outside of various build-ings. Videos of each experiments can be found onthe web (http://ais.informatik.uni-freiburg.de/projects/mvn).They show the incremental update of the final best hy-pothesis. Our current system, though not optimized, is ableto perform an incremental update at a rate of 10Hz ona state-of-the-art desktop computer. The first experimentcontains a trajectory of approximately 1.6 km including 133door actions and is depicted in Figure 4. Given the learnedmotion templates, we were able to detect 125 out of the

133 doors. The pure odometry is shown in Figure 4(a). Inthis experiment, the raw odometry is already good, sincewe intentionally omitted walking around tables and chairswhich would result in high pose errors due to magneticdisturbances. Therefore, a variance of 0.03 m per meter and aN-scan-back of 7 were sufficient to correct the odometry. Wehave chosenλnew = 0.04 since this value is approximatelyobtained by dividing the number of doors by the area coveredthrough the doors. Note, that althoughλnew is dependant onthe building the human is operating in, small changes willnot alter the final outcome. However, if operating in a hotel,λnew should be siginifcantly higher than if operating in awarehouse. Based on the detection and tracking of individualdoors, the map was corrected as depicted in Figure 4(b).Note that we show the maximum likelihood map of the multihypothesis tracking only. Given the free space traversed bythe human and the knowledge that doors separate rooms, wecan enlarge the current trajectory up to a thresholdd = 2.5mto seek for walls, i.e., build a Voronoi diagram, based on allposes within a room (see Figure 4(c)). The resulting map ofthe inner part is depicted in Figure 4(d). For comparison weenlarged the indoor part of this experiment and compared itto a laser map, which is shown in Figure 4(e) and Figure 1.

The second experiment contains a trajectory of approxi-mately 1.3 km and was obtained by walking inside a uni-versity building containing several seminar rooms. Here, weintentionally walked closely around rows of tables and chairs.The magnetic disturbances led to a high pose error, as canbe seen in the raw odometry (see Figure 5(a)). We thereforeused a high variance of 0.2m per meter to make sure that thevariance is not over-confident. Although the initial odometrydiffers up to 30m for the same place, we were able to correctit as shown in Figure 5(b). The map obtained by our approachis visualized in Figure 5(c) and a floor plan of the samebuilding is depicted in (d). In this experiment we detectedall 63 doors up to an accuracy of 0.5 seconds wrt. a manuallylabeled ground truth.

The third experiment contains a trajectory of a personwho is about 20cm taller than the person whose motionswere used for training the templates. The parameters usedto correct this trajectory were the same as for the second

Fig. 5. The second experiment containing a trajectory of about 1.3 km: the raw trajectory is depicted in (a). We intentionally walked closely around tablesand chairs resulting in a high pose error due to magnetic disturbances. The corrected trajectory using our approach is depicted in (b). The approximatemap of the environment and a floor plan of the building are shown in (c) and (d) respectively.

Fig. 6. The third experiment containing the motion from a different person. The raw odometry and the corrected trajectory based on our approach aredepicted in (a) and (b) respectively. The calculated approximate map is shown in (c) and a floor plan of the same building is depicted in (d).

experiment. The outcome of this experiment is shown inFigure 6. Here, 24 out of 27 doors were detected.

VII. C ONCLUSIONS

In this paper, we presented a novel approach for approx-imate mapping of indoor environments using sensed humanmotion. Our approach considers the trajectory of the personas motion constraints and door handling events detectedusing specific motion templates as landmarks within a graph-based SLAM approach. To cope with the high data asso-ciation uncertainty, we employ a multi-hypothesis trackingapproach. Our approach has been implemented and tested onreal data acquired by people walking inside and outside ofvarious buildings. The experimental results demonstrate thatour approach is able to robustly keep track of the true dataassociation and accurately estimate the trajectory taken bythe person. Additionally, we can create approximate maps ofthe environment which accurately resemble the true layouts.

VIII. A CKNOWLEDGMENTS

The authors would like to thank Gian Diego Tipaldi forthe fruitful discussions. We would also like to thank Xsensfor providing us with the additional dataset used in thethird experiment. This work has been supported by the ECunder contract number FP6-IST-034120 Micro/Nano basedSystems

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