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Indoor Mapping and Localization for Pedestrians using OpportunisticSensing with Smartphones
Qing Liang1, Lujia Wang2, Youfu Li3, and Ming Liu1
Abstract— Indoor localization for pedestrians has gainedincreasing popularity among the rich body of literature forthe last decade. In this paper, a low-cost indoor mapping andlocalization solution is proposed using the opportunistic signalsfrom ambient indoor environments with a smartphone. It iscomposed of GraphSLAM-based offline mapping and Bayesianfiltering-based online localization using generated signal maps.The GraphSLAM front-end is constructed by motion con-straints from pedestrian dead-reckoning (PDR), loop-closureconstraints identified by magnetic sequence matching with WiFisignal similarity validation, and observation constraints fromopportunistic magnetic headings after error rejection. Globallyconsistent trajectories are created by graph optimization, afterwhich signal maps (e.g., WiFi, magnetic fields, lights) aregenerated by Gaussian Processes Regression (GPR) for laterlocalization. We propose to use the pseudo-wall constraintsfrom the GPR variance map of magnetic fields and thelights measurements as observations for particle filtering. Theproposed method is evaluated on several datasets collected fromboth the in-compass office buildings and outside public areas.Real-time localization is demonstrated on a smartphone in anoffice building covering 2000 square meters with the 50- and90-percentile accuracies being 2.30 m and 3.41 m, respectively.
I. INTRODUCTION
To meet the ever-rising demand for location-based services(LBSs), low-cost solutions to indoor localization for pedestri-ans have attracted much attention in recent years, especiallywith the rapid development of sensory-rich and computa-tional powerful smartphones [1]. In particular, opportunisticsignals (e.g., WiFi, magnetic fields) have been successfullyexploited for low-cost localization via fingerprinting [2]–[5], due to their pervasiveness and free usage in mod-ern buildings. As for fingerprinting methods, a fingerprintdatabase (a.k.a, signal maps) is required as a prior to archiveonline localization. Manual site surveys are conventionalapproaches to signal map generation. However, they are time-consuming and labor-intensive [6].
∗This work was supported by Shenzhen Science, Technology andInnovation Commission (SZSTI) JCYJ20160428154842603, JCYJ20160401100022706, and JCYJ20170818153518789; Research Grant Councilof Hong Kong SAR Government, China, under project No. 16212815,21202816; partially supported by the HKUST Project IGN16EG12, awardedto Prof. Ming Liu; and was also supported by the National Natural ScienceFoundation of China No. 61603376, awarded to Dr. Lujia Wang.
1Qing Liang and Ming Liu are with Department of ECE, Hong KongUniversity of Science and Technology. [email protected]@ust.hk
2Lujia Wang is with Center for Cloud Computing, Shenzhen In-stitutes of Advanced Technology, Chinese Academy of [email protected]
3Youfu Li is with Department of MBE, City University of Hong [email protected]
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Fig. 1: From left to right: reference walking path labeled ona floorplan for a large area of the HKUST academic building;optimized trajectory by the proposed method and the generated GPRvariance map of magnetic fields as a side product.
To improve survey efficiency, automatic site surveyingapproaches based on crowd-sourcing techniques [6], [7]have been proposed in the mobile computing community.Smartphone users are employed as free site-surveyors tocrowd-source their local sensing data to a public locationservice provider for signal map generation. However, thesemethods suffer from the data-quality control problem causedby heterogeneous devices and unconstrained users [8]. Inaddition, some prior knowledge (e.g., a physical floorplan) ofthe survey area is required to provide ground truth locationsto label signal fingerprints. Yet the floorplans may not bereadily accessible to public users due to privacy concerns.
In the meantime, Simultaneous Localization and Mapping(SLAM) [9] has been exploited by some researchers for sig-nal map generation (or site-survey) and pedestrian localiza-tion [4], [10]–[12]. Alternative to the robot SLAM solutions[13] that normally require information-rich perception data(e.g., 3D point clouds) [14] from expensive sensors (e.g.,Lidar), signal SLAM techniques try to solve the mapping andlocalization problems with inbuilt sensors of mobile devicesthat feature low-quality sensing data. DPSLAM [4] was pro-posed to solve an online SLAM problem using opportunisticsensing on smartphones. However, the high computationdemand renders it less effective in continuous localization onresource-constrained devices. Besides, GraphSLAM-basedoffline site-surveying methods try to simplify the manual-survey process, and accordingly, to reduce the labor and timetaken by surveyors to reach a usable signal map.
WiFi GraphSLAM [10] first formulated the signal SLAMas a GraphSLAM problem. The authors came up with a WiFimeasurement model assuming the interpretability of signalstrength measurements at nearby locations. This assumptionrequires dense WiFi sampling in the testing area to ensure
reasonable interpretability at the cost of more surveyingtime and labor. Pedestrian Dead-reckoning (PDR) [15] is in-volved to provide odometry information. Generally, magneticsignals indoors are good location features benefiting fromthe location-specific and temporal-stable nature induced byanomalies from building construction materials. Gao et al.[11] proposed to use magnetic sequence matching for loop-closure detection within a GraphSLAM framework. GaussianProcesses Regression (GPR) [16] was used to generate prob-abilistic signal maps for later online localization. To rejectfalse loop-closures, the authors used a strong assumption onthe spatial distance between two matched sequences, whichconfined the searching space of each sequence to its spatialvicinity. This may be problematic in large-scale environmentswhere PDR suffers from significant drifts over time. In ad-dition to magnetic matching, authors from [12] incorporatedthe motion constraints from PDR into loop-closure detectionto relax this assumption. Online localization was achievedby Kalman filtering using the generated magnetic maps.
In this paper, we propose a low-cost indoor mapping(site-survey) and localization system for pedestrians usingopportunistic signals perceived by smartphone sensors. Webuild our system on the top of several previous works [10]–[12], and follow the generic framework of GraphSLAM-based offline mapping and Bayesian filtering-based onlinelocalization, yet with remarkable improvements. We usemagnetic sequence matching to identify loop-closures be-tween distinct steps as [11], [12]. The magnetic sequenceis segmented into multiple sub-sequences at turning pointswhich are salient motion patterns for human walking in man-made environments. In addition to the motion pattern, wepropose to use sequence-wise WiFi signal similarities forloop-closure validation in order to reduce false positives.This is very important in large-scale environments wherean increasing number of false positives will arrive due tomismatches of magnetic signals. Additionally, we proposeto involve the opportunistic magnetic headings to the Graph-SLAM front-end by carefully identifying magnetic anomaliesand throwing away error-prone values. The benefits observedare two-fold: 1) global heading information is available forthe optimized trajectory, and 2) map consistency is improvedwith extra measurement constraints.
GPR is used for signal map generation for WiFi signalsand magnetic fields. WiFi maps provide a global locationreference for the mapped area. By contrast, [12] only sup-ported position tracking due to the lack of global references.Different from authors in [11] who declared the useless ofmagnetic maps in localization, we observe that the magneticGPR variance map resembles well the physical floorplanin shape. We propose to use the generated magnetic fieldsvariance map as a “pseudo floorplan” to provide wall-constraints for particle filtering to help the heading and steplength estimation in PDR. Moreover, lights along the walkingpath are identified and then exploited to further constrain thestep lengths. Localization performance will be evaluated ona smartphone in real-time.
We summarize our contributions as follows:
1) WiFi signal similarity validation in aid of magneticmatching-based loop-closure detection and opportunis-tic magnetic heading measurements for the GrapSLAMfront-end construction.
2) Pseudo-wall constraints from generated GPR variancemaps of magnetic fields and light measurements forparticle filtering-based localization.
3) Implementation of the proposed mapping and localiza-tion system, through which globally consistent signalmaps and real-time localization are achieved.
The remainder of this paper is organized as follows. Theoverall system architecture is introduced in Section II. Wedetail the GraphSLAM-based offline mapping in Section III.The particle filtering-based online localization using signalmaps is proposed in Section IV. We present the evaluationresults in Section V and conclude this paper in Section VI.
II. OVERVIEW
The proposed system (Fig. 2) consists of two core functionblocks namely the offline mapping and online localization.
Acc.
Gyro.
Mag.
WiFi
Light
PDR(offline)
Opportunistic magnetic
heading measurement
Magnetic sequence matching-based loop
detection with WiFi similarity validation
Pose graph
optimization
Signal map generation
GraphSLAM front-endGraphSLAM
back-end
Smartphone
sensors
WiFi map Lights map Mag. map
Optimized trajectory
Light detection
PDR(online)
Particle filter
WiFi
Light
Acc./Gyro./
Mag.
Location(x,y)
Heading(theta)
Online localization
Fig. 2: Architecture overview of the proposed system.
Mapping follows the classical GraphSLAM frameworkwith two main components, namely a front-end that pop-ulates an initial pose graph with constraints, and a back-endthat optimizes the graph to be most consistent with theseconstraints. Given the optimized trajectory poses, location-labeled opportunistic signals(e.g., WiFi fingerprints, lights,magnetic field strength) are thus available for map gener-ation, without the needs of a war-driven site survey. Lo-calization follows the standard Bayesian filtering frameworkwhere PDR provides the motion model, and opportunisticsignal measurements with smartphone sensors provide theobservation model with the aid of generated signal mapsin the mapping block. We do not desire an online SLAMrunning on smartphones, as SLAM algorithms are normallymuch more computational heavy than localization itself.Currently, we use a generic particle filter to implement
Bayesian filtering. Some advanced sensor fusion frameworks[17] can be exploited in the future.
III. GRAPHSLAM-BASED OFFLINE MAPPING WITHOPPORTUNISTIC SENSING
In this section, we introduce the GraphSLAM-based of-fline mapping system with an emphasis on the front-end cre-ation. The whole system is composed of: 1) a GraphSLAMfront-end composed of PDR, opportunistic magnetic headingmeasurements, and magnetic sequence matching-based loop-closure detection with WiFi similarity validation; 2) posegraph optimization; and 3) signal map generation.
A. PDRPDR is normally composed of step detection, step length
estimation, and heading estimation. As it is not the focusof our study, we choose a simple PDR algorithm based onzero-crossing detection using inbuilt inertial sensors on asmartphone. To avoid the side effects of magnetic anoma-lies, we use only the accelerometer and gyroscope readingsfor attitude and heading estimation. The step detection isachieved by monitoring the gait cycling pattern exhibitedin vertical acceleration. An empirical step model [18] isused for step length estimation. In our context, PDR-derivedposes are heavily used in constructing the GraphSLAMfront-end as they offer informative motion patterns in aidof magnetic sequence segmentation, opportunistic magneticheading measurements, and motion constraints for the posegraph generation.
B. Magnetic Sequence Matching-based Loop-closure Detec-tion with WiFi Similarity Validation
We first perform turns detection on the PDR derivedtrajectory and then split the whole magnetic sequence intomultiple segments. Loop-closures are identified by magneticsequence matching and then validated by the WiFi signalsimilarity checking to reduce false positives.
1) Turns detection and magnetic sequence segmentation:We assume a space-constraint indoor environment that fea-tures straight corridors interconnected by salient turns (e.g.,left/right/U-turn). This provides geometry constraints to theuser’s walking trajectory, which are crucial to successfulmagnetic matching due to the limited coherence distance ofmagnetic signals. Turn-taking is assumed as a salient motionpattern in such scenarios. We thus exploit these turns tosegment the whole magnetic sequence recorded during thesite survey into sub-sequences for later loop detection.
Normally, turns generate salient variations (e.g., peaksand valleys) on the curvature of the PDR-derived trajectory,which may be trivially identified by peak detection, as shownin Fig. 3a. Some user actions (e.g., side movement duringdoor-opening) may induce false positives. In such cases, wetry to merge two consecutive segments if they are checkedto be in a line. The magnetic field sequence is sliced intomultiple segments as per the detected turning points. Toreduce mismatches, only those informative segments areselected, with sufficient traveling distances and significantsignal variations.
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Fig. 3: (a) Turns (red plus signs) and magnetic headings (bluearrows) marked on the PDR trajectory. The magnified view isfor better visualization of headings. (b) true positive loop closuresshown on the final optimized trajectory stretched along z-axis forbetter visulization. (c) false positives shown on the 2D trajectory.(d) Mismatched magnetic sequences at location A and B.
2) Loop detection using magnetic matching: The mag-netic signals suffer from the spatial sampling density varia-tion problem [3] due to varying walking speeds and samplingfrequencies. To counter this problem, we use dynamic timewarping (DTW) to match these magnetic sequences. It isproven to be an effective tool in measuring similaritiesbetween time-varying signal series. As the user may re-visit the same space in opposite directions, we compareeach pair of magnetic sequences both chronologically andreverse-chronologically. Given the alignment between twomagnetic sequences, the step-to-step correspondences (sayloop-closures) between the walking trajectories can be easilydetermined with timestamps.
Fig. 3a illustrates the correctly detected loop-closures. Thepair-wise loop-closures are shown as blue line segmentsconnecting distinct poses on the optimized trajectory fromone of the collected datasets. These loop-closures exhibitconsiderable spatial consistency between overlapped walkingtrajectories. On the other hand, an increasing number offalse positives may occur in large-scale environments (seeFig. 3b), since magnetic sequence matching is error-pronedue to inevitable magnetic ambiguities (see Fig. 3c). Forinstance, loop-closures between trajectory segments A andB are incorrect as they are indeed on two opposite corridors.The magnetic field sequences are shown in Fig. 3d. The twosequences are very similar to each other in shape, despitethe fairly long samples in use. We will address this problemin the next section.
3) Loop-closure validation using WiFi similarity: Mag-netic signals with anomalies are inherently local features
lacking global uniqueness. Magnetic matching could sufferfrom an increasing number of false positives in large-scaleenvironments. Too many false positives can corrupt theoptimization result. It is important to reduce false positiveswhile maintaining as many true positives as possible.
WiFi signals at faraway locations exhibit poor similaritiesin signal strengths due to the significant signal attenuationin complex indoor environments. The spatial uniqueness ofWiFi measurements improves with the dense deploymentof WiFi APs. Inspired by the complementary nature of thetwo signal modalities, we propose to use the WiFi signalsimilarity as an auxiliary tool to help identify incorrect loop-closures from magnetic matching.
WiFi scans collected along the walking trajectory forma sequence of measurements indexed by timestamps. Tocompare the similarity between two given WiFi sequences,a sequence-wise WiFi signal similarity metric, as illustratedin Fig. 4a, is proposed. A similarity matrix between thetwo sequences is created by computing point-wise signalsimilarities for each pair of samples1. If the two sequencesmatch chronologically as reported by the loop detectionmodule, the average similarity of the diagonal elements in thesimilarity matrix is adopted as the sequence-wise similarity.Otherwise, the back-diagonal elements are used, as shown inFig. 4b. Two examples of the similarity matrix are illustratedin Fig. 4c and Fig. 4d, which are created by two pairs of WiFimeasurement sequences with false-positive and true-positiveloop-closures, respectively.
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Fig. 4: (a) and (b) show the proposed similarity metric betweentwo WiFi sequences, where we have M = 4 samples in the querysequence and N = 5 samples in the template sequence. (c) showsthe case of mismatching and (d) correct matching.
As for the case in Fig. 3b, the mismatched loop-closurescan be identified by the proposed sequence-wise WiFisimilarity. Admittedly, it is not the case for false loop-closures created between two poses at nearby locations due
1We use the Gaussian kernel distance to measure the pair-wise signalsimilarity between two WiFi scans.
to WiFi’s poor spatial resolution. However, the proposedstrategy works reasonably well in rejecting large amountsof incorrect loop-closures on real-world datasets.
C. Opportunistic Magnetic Heading Measurements
In this section, we consider another case where geomag-netic fields dominate ambient magnetic signals. Note that thetypical coherence distance of magnetic fields is around 30 cm[11]. This reveals that the magnetic field from given magnetsources varies fast across space. We observe that magneticsignals measured by a smartphone held by a walking userare fairly stable and uncorrupted in open spaces and widecorridors. This is because in such circumstances smartphonesensors are far away from the steel building materials, e.g.,in the supporting pillars. Magnetic signals with rich localanomalies are good candidates for loop-closure detectionas they are more spatially unique and informative. On thecontrary, undisturbed magnetic signals can provide globalheading measurements relative to Earth. To our knowledge,these measurements have rarely been used in previous worksto create the GraphSLAM front-end.
Algorithm 1 Opportunistic magnetic heading measurements.
Input:Raw readings: acc. {atn}
Nn=1, gyro. {wtn}
Nn=1, and mag.
{mtn}Nn=1, number of sensor samples N ;
PDR odometry: {(δθtk , `tk)}Kk=1, number of steps K;Output:
Opportunistic magnetic headings Θ at certain steps S;1: compute orientations {θmag
tn }Nn=1 relative to MagneticNorth using atn , wtn , and mtn ;
2: for each n ∈ [1, N ] do3: sinθn ← sin
(θmagtn
), cosθn ← cos
(θmagtn
);
4: end for5: Θ← ∅, S ← ∅, winsize = 76: for each k ∈ [1, K − winsize+ 1] do7: 〈rot angle, arc len〉 ←
getCurvature({(δθti , `ti)}k+winsize−1i=k );
8: std sinθ ← std({sinθn}tk≤tn≤tk+winsize−1
);
9: std cosθ ← std({cosθn}tk≤tn≤tk+winsize−1
);
10: θ ← averageAngle({θmag
tn }tk≤tn≤tk+winsize−1
);
11: if rot angle, arc len, std sinθ, and std cosθ meetcertain thresholding conditions; then
12: S ← S ∪ {k}, Θ← Θ ∪ {θ};13: end if14: end for15: return S, Θ;
We opportunistically derive the magnetic headings whenwe identify that the local magnetic disturbance is neglectable.The proposed algorithm 1 is straight-forward yet effective inpractice. The intuition here is that if we keep walking along astraight line (say corridors), the magnetic heading should beconstant in addition to bounded variations due to a human’swalking pattern. This is the case when geomagnetic fieldsdominate the instant magnetic signals. We may safely assume
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Fig. 5: Samples of generated signal maps. (a) detected lights marked with red triangles in the light intensity signal and clustered locationsshown with red circles on the optimized trajectory; units of colorbar: µT for magnetic maps (b) and dBm for WiFi maps (c).
that the magnetic signals are undisturbed if the magneticheading measurements are stable over a period of time duringa straight line walking.
We adopt Madgwick’s AHRS (a.k.a, attitude and headingreference system) solver [19] to compute magnetic headingsincorporating magnetometer readings. The derived magneticheadings are error-prone due to local anomalies. To rejecterrors, we exploit the motion patterns derived by PDR. Weiterate over the derived poses and compute the instant curva-ture of the walking trajectory within a sliding window alongwith the variations of magnetic headings. We assert a straightwalking pattern if the curvature is fairly small. Furthermore,the magnetic heading is assumed to be correct with fairconfidence once we identify bounded heading variations. Toaccount for the angle singularity problem (transition from−π to π and vice versa), we use the sin and cos values forvariation calculation instead. An example is shown in Fig.3a with opportunistic headings marked with blue arrows.
D. Pose Graph Optimization
The initial pose graph is populated with motion constraintsfrom PDR, loop-closure constraints from magnetic sequencematching, and global heading constraints by opportunisticmagnetic heading measurements. Then a pose graph opti-mization back-end is applied to optimize the poses to be mostconsistent with the constraints. We use g2o [20] to implementthe graph optimization. There are still false positive loop-closures even after the global constraints validation in large-scale environments. The robust kernels are exploited tocounter this problem in g2o.
E. Singal Map Generation
The optimized trajectory from the GraphSLAM back-end provides the “ground truth” locations for opportunisticsignals collected during walking. The location-labeled signalfingerprints are then used for signal map generation. Anexample of signal maps is illustrated in Fig. 5. As for WiFiand magnetic fields, GPR proves to be effective in signalmap generation. The generated GPR maps include both themean and variance maps. We do not use the magnetic fieldmap directly for localization since it is difficult to achieve acomprehensive magnetic map using only the measurementscollected along the walking traces [11]. However, the vari-ance map for magnetic signals approximately matches thephysical floorplan. Considering that wall-constrained particle
filters show great effectiveness in reducing PDR drifts, wepropose to use the generated magnetic variance map as apseudo floorplan to help with localization. The lights arefirst identified by peak detection (Fig. 5a). We assume thateach light is revisited a few times, and then cluster theseinitially detected lights according to their spatial locations.
IV. LOCALIZATION WITH PARTICLE FILTER USINGSIGNAL MAP
In this section, an approach to using the generated signalmap for real-time localization is described.
A. Pseudo-wall and Lights Constraints
We do not assume the availability of floorplans. Instead,the magnetic variance map generated previously by GPR isutilized to provide “pseudo-wall constraints” to help with theheading and step length estimation. As is illustrated by 5b,the variance map of magnetic fields indicates approximatelythe traversable area. The regions with small and large vari-ances resemble the mapped corridors and unmapped areas,respectively. Aside from the pseudo wall constraints from themagnetic variance map, the lights along the corridor providemore constraints on the step lengths of particles and helpspeed up the convergence of particle filter.
step length too long
step length too short
lights
wall
particlescloud
particlesviolatingconstraints
heading with large errors
A
B
C
D
A
B
C
A
B
1) 2) 3)
Fig. 6: 1) particles with large heading errors hit the wall and perishwhile evolving from A to D; 2) particles with either too long ortoo short step lengths hit the wall when taking a turn; 3) particlesviolating light constraints get killed at each light detection event.
Fig. 6 demonstrates how the wall constraints and lightconstraints work on heading and step length estimation.Intuitively, particles with correct headings will survive aftera period of straight walking, while those with large heading
errors are most likely to be killed in importance samplingas they violate the wall constraints(Fig. 6-1). The particlecloud is shrunk in the lateral direction (say perpendicular tothe walking direction), but elongated due to multi-hypothesisof step lengths. Particles with longer step lengths move fasterthan those with shorter step lengths. When the user takes aturn, particles will violate the wall constraints and get killed,whose step lengths are either too long or too short(Fig. 6-2). Once a light is detected (Fig. 6-3), particles are killedbeyond the lights’ coverage vicinity. Similar to the effect ofwall constraint during taking turns, only those particles withmoderate step lengths are likely to survive.
B. Particle Filtering
Each particle maintains a hypothesis of the state X =(x, y, θ, `) that involves the user’s 2D position (x, y), headingθ, and step length `. The particle state gets updated ateach step k taken by the user incorporating the motionmeasurements from PDR, namely step length estimate ˆ
k andheading change estimate δθk. When new observations(e.g.,WiFi scan, lights detection) arrive, weights for each particleare updated and a new set of particles will be generatedfrom the previous through importance sampling if necessary.The state estimation is achieved by taking the centroid ofcurrent particle clouds at each step. We accommodate thebasic particle filtering procedure.
Initialization: To enable global localization, we use theMaximum Likelihood Estimation (MLE) with WiFi finger-printing to provide the initial location guess, after whichparticle filtering is used for position tracking. Even thoughit is possible to rely on particle filtering without point-mass initialization to achieve global localization, the conver-gence speed is not guaranteed especially when running ona resource-constrained smartphone. To further improve thefiltering convergence, the noisy magnetic heading is used tonarrow down the initial heading guess.
Weight Update: The overall weights wik are updated by a
few separate importance weights indicating how likely it isthat the measurements are consistent with the current particlestates. The generic importance weight for the ith particle isκi ∝ P
(zik|Xi
k
). The weights for the WiFi observation,
lights observation, and pseudo wall constraints are shown inthe following:
κWiFii ∝ exp
NWiFi∑j=1
(zWiFij − zWiFi
j )2
−2(σWiFij )2
, (1)
where zWiFij and zWiFi
j are the predicted signal measurement andthe observed for the jth WiFi access point (AP), respectively,NWiFi is the number of observed APs in common, and (σWiFi
j )2
is the predicted variance from the WiFi variance maps;
κlighti ∝ exp
min
1≤j≤N lightd2j
−2σ2light
, (2)
where dj is the predicted distance of the current particle fromthe jth light in the lights map, N light is the number of lights,and σ2
light indicates the coverage of each light;
κwalli ∝ exp
{σ2
magn
−2σ2wall
}, (3)
where σ2magn is the predicted magnetic signal variance at the
current particle location, and σ2wall indicates the penalty on
particles that violate the pseudo wall constraints. Intuitively,the penalty is in negative correlation with σ2
wall.These weights are normalized to preserve a valid prob-
ability distribution over all the particles. If some measure-ments are unavailable for the current step, the correspondingweights are uniformly assigned. For instance, the lights-based weight update is only triggered each time a light spotis detected. In the end, the total weights are updated as perwi
k = wik−1×ηκWiFi
i κlighti κwall
i , where η is a normalization factor.In our implementation, we choose σlight = 2 m, σwall = 5µT.
V. EVALUATION
Six datasets are collected from various indoor environ-ments including office buildings in the HKUST campus anda shopping mall outside, with walking traces lasting from 10min to 20 min and coverage areas from 2000 m2 to 4000 m2
approximately. An Android smartphone (model SamsungGalaxy S5) is used both for data collection and localizationevaluation throughout this paper. The surveyor is asked tosimply hold the phone and walk.
A. Mapping Results
1) Loop detection with validation: The statistics on loop-closure correctness is summarized in TABLE I as per thetotal number of True Positives (TPs) against False Positives(FPs) for each dataset using the final optimized trajectoriesas ground truth. False positives are reduced to the minimumafter the validation which proves the effectiveness of the pro-posed WiFi similarity-based validation method. We choose astrict similarity checking threshold that successfully rejectedall the FPs yet at the cost of throwing away a certain portionof TPs. A less strict threshold could be feasible if a morerobust back-end is used.
TABLE I: Statistics on TPs/FPs of the detected loop-closures beforeand after WiFi similarity validation
Dataset CYT AC3-1 AC3-2 AC4-1 AC4-2 SOGO
Before 2156289
1038873
322215
11622430
61080
223157
After 13660
9160
3220
5910
6100
1130
2) Trajectory estimation: Fig. 7 only show the results forthree more datasets in addition to those already shown in Fig.1 and Fig. 5 due to limited space. The raw PDR paths revealthe drift-prone nature of PDR, especially with the low-costsmartphone sensors. However, the relative motion patternsare correctly measured such as the long straight walking andleft/right/U-turns. As shown in Fig. 7, optimized trajectoriesare not aligned correctly to the Earth frame without magnetic
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Fig. 7: From top to bottom: AC3-2, AC4-1, and SOGO. From left to right: 1) raw PDR-derived trajectories before optimization, 2)optimized trajectories without incorporating opportunistic magnetic heading measurements, 3) the final optimized trajectories by theproposed method, and 4) reference paths on the floorplan. The floorplan is used only for visuallization throughout this paper.
headings measurements. In some cases, they are not evenglobally consistent rendering them useless for signal mapgeneration. However, the final optimized trajectories showconsiderable consistency to physical floorplans.
TABLE II: Statistics on the ROE errors
Dataset CYT AC3-1 AC3-2 AC4-1 AC4-2 SOGO
Error/m 0.87 2.44 2.99 8.10 6.67 22.32
Due to the lack of ground truths, we characterize “howcorrectly the user returns back to the origin” as Return-to-Origin Error (ROE). The statistics are shown in TABLE II.The ROE error 0.87 m is considerably small for CYT. Wecredit this to the rich magnetic anomalies induced by theconstruction materials in the building, working electronicequipment from inside labs, and narrow corridors that confinethe walking trajectory taken each time. The ROE errors forother office areas range from 2.44 m to 8.10 m mainly due tovarying coverage area and amounts of magnetic anomalies.The ROE error 22.32 m for SOGO is significantly large dueto poor magnetic anomalies found in this area with widecorridors and inconsistent walking trajectories caused by therandom interferences from the crowded people around.
B. Localization Results
Localization is tested with the generated signal mapsfrom the dataset CYT, and a real-time localization demo isshown in the supplementary video. 1000 particles are usedthroughout this experiment. The results are highlighted inFig. 8a by the blue lines which show sound consistency
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Fig. 8: Illustration of the localization results with (a) and without(b) lights measurements. The testing walk originates from the left-bottom corner with the square marking the start point and the circlemarking the endpoint.
to the reference path. The WiFi MLE results are shown asred plus signs that are scattered through the region due tolack of motion and wall constraints. The trajectory from aProject Tango device is also plotted against the floorplan forvisual comparison. We observe clear drifts in Tango’s pathas is highlighted by the magnified view in Fig. 8a while
the results of the proposed method are constrained well inthe corridor region. This reveals that the proposed particlefiltering method with signal maps could achieve consistentlocalization over time. Localization without light constraintsis shown in Fig. 8b. The performance degradation is due tothe lack of enough constraints on step lengths.
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Fig. 9: Localization error CDF.
We placed 25 check-points along the corridor and derivedtheir ground truth locations using a floorplan. The cumulativedistribution function plot (CDF) is then generated for erroranalysis, as shown in Fig. 9. The 50-percentile and 90-percentile accuracies are summarized in TABLE III for threelocalization algorithms namely WiFi MLE, particle filteringwithout and with light measurements. We observe significantlarge errors (32 m, see the dash-dot line in Fig. 9) for theproposed method with light detection disabled. This is dueto the recursive nature of particle filtering where real-timelocalization results normally improve with time. The initialerror is likely to be unacceptable but will be reduced to abounded value once the particle filter coverages. We take twoconsecutive rounds for the final proposed method. Particlefiltering with the generated WiFi maps and the “pseudofloorplan” shows significantly better performance than WiFiMLE. The performance is further improved with additionalconstraints from the lights map on step lengths.
TABLE III: Localization accuracy statistics
Algo. MLE PF-w/o PF-w/ (1st) PF-w/ (2nd)
50-ile Error/m 5.35 3.04 2.45 2.30
90-ile Error/m 9.52 5.97 5.46 3.41
VI. CONCLUSIONS
In this paper, a low-cost indoor mapping and localizationsolution was proposed using the opportunistic signals fromambient environments with a smartphone. The system wassplit into two blocks namely offline mapping within theclassical GraphSLAM framework and online localizationwith Bayesian filtering using the generated signal maps.The proposed WiFi similarity validation for loop-closuredetection showed great effectiveness in our implementationwhere false positives were significantly rejected. The incor-poration of opportunistic magnetic heading measurements
improved the GraphSLAM-based trajectory optimization andhelped generate more consistent maps. Real-time localiza-tion on smartphones was achieved through particle filteringwith the generated signal maps. The pseudo-wall constraintsfrom the magnetic fields GPR variance map and the lightsconstraints worked well in confining the particle clouds.The proposed system was thoroughly evaluated on severaldatasets collected from both the in-compass office buildingsand off-compass public areas where globally consistent tra-jectories were reached. Real-time localization performancewas evaluated in an office building with a coverage area of2000 m2. The 50- and 90-percentile accuracies achieved ona smartphone were 2.30 m and 3.41 m respectively.
REFERENCES
[1] L. Pei, M. Zhang, D. Zou, R. Chen, and Y. Chen, “A survey ofcrowd sensing opportunistic signals for indoor localization,” MobileInformation Systems, vol. 2016, 2016.
[2] Y. Sun, M. Liu, and M. Q.-H. Meng, “WiFi signal strength-based robotindoor localization,” in Proc. ICIA. IEEE, 2014, pp. 250–256.
[3] Y. Shu, C. Bo, G. Shen, C. Zhao, L. Li, and F. Zhao, “Magicol:Indoor localization using pervasive magnetic field and opportunisticWiFi sensing,” IEEE J. Sel. Areas Commun., vol. 33, no. 7, pp. 1443–1457, 2015.
[4] R. Faragher, C. Sarno, and M. Newman, “Opportunistic radio SLAMfor indoor navigation using smartphone sensors,” in Proc. PLANS.IEEE, 2012, pp. 120–128.
[5] Q. Liang and M. Liu, “Plugo: A VLC systematic perspective of large-scale indoor localization,” arXiv preprint arXiv:1709.06926, 2017.
[6] C. Wu, Z. Yang, and Y. Liu, “Smartphones based crowdsourcing forindoor localization,” IEEE Trans. Mobile Comput., vol. 14, no. 2, pp.444–457, 2015.
[7] A. Rai, K. K. Chintalapudi, V. N. Padmanabhan, and R. Sen, “Zee:Zero-effort crowdsourcing for indoor localization,” in Proc. Mobicom.ACM, 2012, pp. 293–304.
[8] C. Gao and R. K. Harle, “Semi-automated signal surveying usingsmartphones and floorplans,” IEEE Trans. Mobile Comput., 2017.
[9] C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira,I. Reid, and J. J. Leonard, “Past, present, and future of simultaneouslocalization and mapping: Toward the robust-perception age,” IEEETrans. Robot., vol. 32, no. 6, pp. 1309–1332, 2016.
[10] J. Huang, D. Millman, M. Quigley, D. Stavens, S. Thrun, andA. Aggarwal, “Efficient, generalized indoor WiFi graphSLAM,” inProc. ICRA. IEEE, 2011, pp. 1038–1043.
[11] C. Gao and R. Harle, “Sequence-based magnetic loop closures forautomated signal surveying,” in Proc. IPIN. IEEE, 2015, pp. 1–12.
[12] S. Wang, H. Wen, R. Clark, and N. Trigoni, “Keyframe based large-scale indoor localisation using geomagnetic field and motion pattern,”in Proc. IROS. IEEE, 2016, pp. 1910–1917.
[13] I. Deutsch, M. Liu, and R. Siegwart, “A framework for multi-robotpose graph SLAM,” in Proc. RCAR. IEEE, 2016, pp. 567–572.
[14] M. Liu, F. Pomerleau, F. Colas, and R. Siegwart, “Normal estimationfor pointcloud using GPU based sparse tensor voting,” in Proc.ROBIO. IEEE, 2013, pp. 91–96.
[15] R. Harle, “A survey of indoor inertial positioning systems for pedes-trians,” IEEE Commun. Surveys Tuts., vol. 15, no. 3, pp. 1281–1293,2013.
[16] K. Qiu, F. Zhang, and M. Liu, “Visible light communication-basedindoor localization using Gaussian process,” in Proc. IROS. IEEE,2015, pp. 3125–3130.
[17] M. Liu, L. Wang, and R. Siegwart, “DP-Fusion: A generic frameworkfor online multi sensor recognition,” in Proc. MFI, 2012, pp. 7–12.
[18] H. Weinberg, “Using the ADXL202 in pedometer and personal navi-gation applications,” Analog Devices AN-602 application note, vol. 2,no. 2, pp. 1–6, 2002.
[19] S. O. Madgwick, A. J. Harrison, and R. Vaidyanathan, “Estimation ofIMU and MARG orientation using a gradient descent algorithm,” inProc. ICORR. IEEE, 2011, pp. 1–7.
[20] R. Kummerle, G. Grisetti, H. Strasdat, K. Konolige, and W. Burgard,“g2o: A general framework for graph optimization,” in Proc. ICRA.IEEE, 2011, pp. 3607–3613.
