Proceedings of: 11th International Work-Conference on Artificial Neural Networks (IWANN 2011). International Workshop of Intelligent systems for context-based information fusion (ISCIF 11). Torremolinos-Málaga, Spain, June 8-10, 2011Many works on context-aware systems make use of location, navigation or tracking services offered by an underlying sensor fusion module, as part of the relevant contextual information. The obtained knowledge is typically consumed only by the high level layers of the system, in spite that context itself represents a valuable source of information from which every part of the implemented system could take benefit. This paper closes the loop, analyzing how can context knowledge be applied to improve the accuracy, robustness and adaptability of sensor fusion processes. The whole theoretical analysis will be related with the indoor/outdoor navigation system implemented for a wheeled robotic platform. Some preliminary results are presented, where the context information provided by a map is integrated in the sensor fusion system.This work was supported in part by Projects ATLANTIDA, CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAM MADRINET S-0505/TIC/0255 DPS2008-07029-C02-02.Publicad

A. Nemra

A.M. Bernardos

E. Martí

F. Castanedo

F. Gustafsson

J. Gómez-Romero

J. Wendel

L. Jing

M.E. Liggins

R. Horaud

Martí, Enrique David

García, Jesús

Molina, José M.

Name not available

Context-Awareness at the Service of Sensor Fusion Systems: Inverting the Usual Scheme

e-Archivo (Univ. Carlos III de Madrid e-Archivo)

This document is published in:Cabestani, J. et al. (eds.) (2011) Advances in Computational Intelligence: 11th International Work-Conference on Artificial Neural Networks, IWANN 2011, Torremolinos-Málaga, Spain, June 8-10, 2011, Proceedings, Part II.(Lecture Notes in Computer Science, 6692). Springer,  653-660. DOI: http://dx.doi.org/10.1007/978-3-642-21498-1_82© 2011 Springer-Verlag Berlin HeidelbergContext-Awareness at the Service of SensorFusion Systems: Inverting the Usual SchemeEnrique Mart´ı, Jesu´s Garc´ıa, and Jose Manuel MolinaApplied Artificial Intelligence Group, Universidad Carlos III de Madrid,Av. de la Universidad Carlos III, 22, 28270, Colmenarejo, Madrid, Spainemarti@inf.uc3m.es, jesus.garcia@inf.uc3m.es, molina@ia.uc3m.es,http://www.giaa.inf.uc3m.esAbstract. Many works on context-aware systems make use of location,navigation or tracking services offered by an underlying sensor fusionmodule, as part of the relevant contextual information. The obtainedknowledge is typically consumed only by the high level layers of thesystem, in spite that context itself represents a valuable source of in-formation from which every part of the implemented system could takebenefit. This paper closes the loop, analyzing how can context knowl-edge be applied to improve the accuracy, robustness and adaptability ofsensor fusion processes. The whole theoretical analysis will be relatedwith the indoor/outdoor navigation system implemented for a wheeledrobotic platform. Some preliminary results are presented, where the con-text information provided by a map is integrated in the sensor fusionsystem.Keywords: sensor fusion, navigation, indoor, outdoor, context-aware,particle filter, software agent.1 IntroductionSensor Fusion is a process that consists in combining observations provided byseveral sensors about an entity of interest, so that the information finally ob-tained is better —in some sense— than what could be inferred by taking eachof the sensors alone. This task is just a part of the more general concept of DataFusion which, among other particularities, is not limited to sensing information.Location and tracking of dynamic objects [11][13][8][2][5] can be accounted asone of the most important applications of sensor fusion. Solving this problemrequires a clear specification of, at least, what has to be estimated (variables ofinterest about the observed system), data provided by sensors, and how bothsystem state and sensor readings are related. Fusion performance can be bene-fited of any additional information, such as a mathematical model of observedsystem dynamics. Fusion processes in real scenarios are, however, affected by avariety of external factors that cannot be accounted while modeling the problem,either because are subject to uncontrolled changes over time, or because theyare even unknown to us.1To overcome that hurdle, fusion systems should be able to detect relevantchanges in their environment, and adapt themselves for achieving the best per-formance: this is, context-aware sensor fusion. Most of the existing literatureabout context-aware applications featuring location and/or navigation followthis scheme in the opposite direction: fusion module performs location, andthe obtained result is employed as a position-based context for higher-levelapplications [1][12].The discipline of Data Fusion represents an example of fusion processes re-sponsive to their environment. In the JDL model [9] for data fusion systems, the4 level (Process Refinement) describes how to use the acquired information tofeedback the lower levels by means of, for instance, sensor retasking or modelmodification. The goal of this paper is to analyze the direct application of con-textual information to sensor fusion tasks. Many of the provided examples arebased on the platform used in the experimental part, which was introduced in[10] and is briefly described in section 2. It consists in an autonomous robot thatperforms indoor and outdoor navigation using a variety of onboard and externalsensors, enabling the scenario for representing a full scale fusion problem. Fol-lowing that, section 3 conduct a theoretical analysis regarding the applicabilityof contextual information in sensor fusion processes. It begins covering the topicof modeling environment for context acquisition and processing. Immediatelyafterwards, a second part identifies the parts in a sensor fusion process wherethe obtained contextual information can be applied. Finally, some preliminaryresults using the described platform are presented in section 4. They show howsensor fusion can benefit from the use of contextual information either improv-ing accuracy or reducing the computational burden of selected algorithms. Someremarks and conclusiones are given in the last section.2 Sample ScenarioThe scenario selected as reference for the analysis conducted in next sectionis the problem of combined indoor/outdoor navigation: estimation of position,orientation and dynamics of a robot, which is equipped with onboard sensors butalso features communication capabilities with other entities that will serve asexternal sensors. From the architectural point of view, this navigation system isorganized in layers in order to maximize its flexibility. Each tier plays a differentrole in the process of acquiring and transforming information into somethinguseful for the final data sink: a Particle Filter which fuses all the informationinto the most likely estimation. One of the strongest reasons for selecting sucha solution is to provide a reasonably complete scenario that does not limit theperformed theoretical analysis.Figure 1 contains a schematic view of the system. Information flows top-downin the diagram, with sensor data represented by small triangles. Upper levelsare in charge of capturing information either by means of sensors physicallyattached to the platform or by exchanging data with external intelligent entities.The sensor abstraction layer is in charge of managing physical sensors as well2Fig. 1. Architecture of the proposed navigation system for an individualmobile platformas providing a unified vision of external information sources. The applications of contextual information to sensor fusion described in section 3 involve acting over this layer.The Intermediate reasoning layer receives and process raw sensor measures.The operations hosted by this level range from adapting sensing informationto meet the various requirements of the filter, to more advanced inferences asderiving context information from the available readings. The diagram showsonly the box corresponding to context reasoning for the sake of clarity. The lastlevel contains simply a filtering-capable algorithm for integrating the incomingdata.3 Theoretical AnalysisThis section is divided in two parts. The first one reviews different representa-tions of environment for extracting contextual information, and the second detailswhere and how this information can be used within a sensor fusion system.3.1 Knowledge about Environment and Context RepresentationLet us define the environment of an application as the multidimensional spacewhere it operates, including other variables which an influence on the problembeing solved. Those variables can be categorized in different ways, for instance,if we focus on what that knowledge is referred to, we can discern between in-formation about the environment itself and information about the different en-tities populating it. Inside the first category we can account for several types ofinformation attending to its nature.3If we are talking about either continuous or discrete valued variables thathave a defined value for every point of the environment (i.e. fields), then themost straightforward representation is a map. Examples of this type of variablesare ambient temperature, obstacle location or signal/noise level for a certainelectromagnetic emission.Some information which is not likely to be mapped, can be represented bystatements instead (i.e. declarative knowledge). The weather is a perfect exampleof propositional context knowledge: sunny or rainy condition, current wind speed,etc. are factors to take into account in sensor fusion becasue they can affect theperformance of some devices. Knowledge about external entities also lays in thefield of statement-based information, but is a bit trickier because it tends toinvolve complex reasoning processes. Entity-related knowledge can be classifiedin two general families: feature and relational knowledge.Among the many examples of feature knowledge we can cite identity, position,activity and utility. Although the extraction of such features can involve complexdata structures and intrincated processing schemes —some of them still an openresearch field, as activity recognition—, they can be represented as value tuplesor simple labels once have been determined. Relational knowledge describes thedifferent interactions or links between entities. Ontologies for entity and graphsfor groupal activity recognition can be enclosed in this category.3.2 Applying Context to Sensor FusionThe acquired contextual information can be injected in differents places of asensor fusion system. The two principal insertion points are the set of sensorsand the fusion algorithm. The first category, acting over the sensors, includes atleast four uses of context information: sensor selection, modification of captureparameters, modification (correction) of raw sensing data, and finally sensingdata augmentation (complete it with new information). The two first types arecommonly known as sensor retasking in terminology of distributed multisensordata fusion. Nonetheless, they can be important features also in centralized,simple sensor fusion systems. For instance, selection plays an important rolewhen dealing with redundant sets of sensors.Sometimes direct observations can provide the required context, as in the caseof fusing video and infrared sensors: in spite that both provide spatial informa-tion about non-occluded surfaces in the environment, poor lighting conditionsdiscards a video camera as an effective sensor, while an infrared sensor can beaffected by colors and reflection angles. Both effects can be detected using thesole video input. Another example is shown in figure 2. The motion of the roverin a rough floor cause vibrations which spoil the measures, as seen around sec-ond 9. On the other hand, some effects are not as easy to detect. A sustainedmagnetic interference can bias the readings of a magnetometer, but figuring outthe existence of this problem requires further estimations processes and contextdata.4? ? ? ? ? ?? ?? ?? ?? ?? ??????????????????????????????????????????????????????????????????????? ?????????? ???????????Fig. 2. Vibrations due to robot motion have a harsh effect on inertial unit measuresRegarding the modification of capturing parameters, we can cite changing theorientation of directional sensors such as PTZ video cameras. Existing algorithmsfor visual attention [7] and tracking can provide the information needed for thechange of parameters.The third option is to modify the sensed data to correct undesired effects.As an example, mobile entities can degrade the performance of map matchingalgorithms, because they affect laser readings. However, the context can be usedto identify and remove the spurious beam hits. Finally, sensor data can be aug-mented by including additional data to be considered in the fusion process, asthe confidence in a measure or a label indicating how to process it.The second entry point for context information in sensor fusion systems isthe proper fusion algorithm, by means of selection —change algorithm— andmodification —manipulation of parameters—. Examples of algorithm selectioncan be employing a Particle Filter for indoor navigation, where walls and otherobstacles make the problem highly nonlinear, but switching to a simpler and lesscostly approach such as least squares or a Kalman-like filter in open spaces. Mul-tiple model systems and particle filters with adaptive population size constituteexamples of algorithm modification.4 Experiments on NavigationThis part of the paper gathers empirical results obtained with the platform de-scribed in section 2. A first subsection will thoroughly describe the configurationemployed in the experiments, immediately followed by the obtained results alongwith an analysis on them.4.1 Navigation System SetupThe experiments presented in this paper have been reproduced in laboratoryfrom both simulated and real data. The real data has been obtained in con-trolled experiments where the robot was been equipped with a GPS sensor withmeter-level precission and an inertial measurement unit (IMU). With simulationpurposes, GPS measures are assumed to suffer a random gaussian-like noise withstandard deviation of 1 meter.5The baseline navigation algorithm relies in a Particle Filter which performsloosely coupled fusion of the two proposed sensors. This approach will be com-pared with a similar system that also includes the information of a map. It mustbe noted that the available set of sensors do not allow the implementation ofmap matching techniques [6][3] that, provided with an almost perfect map, canresult in an outstanding positioning performance. Instead, the map is used todiscarding particles that move into a wall, as done in [4].The system is tested in a very simple porch-like scenario. It presents obstaclesto be mapped, while being an almost open space with avaialble GPS signal. Therobot navigates in a relatively reduced space that, provided the low accuracy ofGPS measures, makes the problem more difficult to solve.4.2 Obtained ResultsThe conducted experiments compare navigation performance with and withouta map of obstacles. Using standard sensing conditions, with update rates around30 Hz for the IMU and 1 Hz for the GPS, the map-less navigation algorithmusually results in an average positioning error of 0.4 meters. This represents a65% error decrease compared with the average 1.2 m error of bare GPS measures.Figure 3 shows the filtered trajectory for one of the runs. The slash-markedpath represents the true trajectory of the robot, while the track of dense smallcircles represent the Particle Filter estimation. GPS measures are marked as largecircles, while the cloud of points with attached lines represent the position andorientation of particles at the end of the simulation. When using a map, the algo-rithm not only worsens its position estimation (0.6 meters, 50% improvement),but also spoils the continuity of the estimated trajectory with sudden jumps, asshown in figure 4. They are a direct effect of particle anihilation together withbiased GPS measures. The real improvement, as in many sensor fusion systems,comes by the hand of degraded sensor performance. For the following experi-ments, degraded sensing conditions were assumed: IMU readings feature a noisecharacteristic of rough terrain, and GPS measures lower the update rate to 0.2Fig. 3. Navigation without map, assuming best sensing conditions6Fig. 4. Navigation with map, assuming best sensing conditionsHz as in the case of occlusions. Under these conditions, taking into account map information leads to slightly better results. The baseline navigation algorithm have a mean position error of 1.2 meters, same as GPS measures, while including the map results in about 0.9 meters, although its stimate is still less smoother than the basic version.In spite that the performed experiments are quite reduced, they show a funda-mental fact: context information, when adecquately integrated into sensor fusionsystems, can improve their robustness under conditions of degraded sensing per-formance. This is of vital importance for systems pretending to be autonomousand work unattended for long periods of time.5 ConclusionsThe contribution of this paper is two-fold. On the one hand, the theoreticalanalysis in section 3 tries to conciliate the worlds of Data Fusion and context-aware applications. It can be seen that all provided examples, which have beenrelated to the sample scenario but can also be found in existing literature, solveproblems using sensor fusion systems and context information. The problem isthat authors are usually very focused in the context part and do not make use ofthose formalisms developed within Data Fusion field. Integrating both disciplinescan lead not only to better results, but also to a faster progress thanks to notreinventing existing concepts. The other contribution is an scheme defining howartificial intelligence applications can feedback their sensor fusion modules in or-der to improve their results. Some preliminary experiments on indoor/outdoornavigation are also presented, where the simple use of a map provides the neces-sary context to improve location accuracy under degraded sensor performance.The obtained results are far from being spectacular in aboslute terms, but it isimportant to take into account that the goal was to test if even a very weak useof context information could serve to improve the performance of a sensor fusionsystem.7AcknowledgementsThis work was supported in part by Projects ATLANTIDA, CICYT TIN2008-06742-C02-02/TSI, CICYT TEC2008-06732-C02-02/TEC, SINPROB, CAMMADRINET S-0505/TIC/0255 DPS2008-07029-C02-02.References1. Bernardos, A.M., Tarrio, P., Casar, J.R.: A data fusion framework for context-aware mobile services. IEEE, Los Alamitos (2008)2. Castanedo, F., Garc´ıa, J., Patricio, M.A., Molina, J.M.: Data fusion to improvetrajectory tracking in a Cooperative Surveillance Multi-Agent Architecture. Infor-mation Fusion 11(3), 243–255 (2010)3. Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte Carlo localization for mo-bile robots. In: Proceedings 1999 IEEE International Conference on Robotics andAutomation (Cat. No.99CH36288C), pp. 1322–1328 (February 2001)4. Evennou, F., Marx, F., Novakov, E.: Map-aided indoor mobile positioning systemusing particle filter. In:IEEE Wireless 2005 (2005)5. Go´mez-Romero, J., Patricio, M.A., Garc´ıa, J., Molina, J.M.: Ontology-based con-text representation and reasoning for object tracking and scene interpretation invideo. Expert Systems with Applications 38(6), 7494–7510 (2010)6. Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson,R., Nordlund, P.-J.: Particle filters for positioning, navigation, and tracking. IEEETransactions on Signal Processing 50(2), 425–437 (2002)7. Horaud, R., Knossow, D., Michaelis, M.: Camera cooperation for achieving visualattention. Machine Vision and Applications 16(6), 1–2 (2005)8. Jing, L., Vadakkepat, P.: Interacting MCMC particle filter for tracking maneuver-ing target. Digital Signal Processing 20(2), 561–574 (2010)9. Liggins, M.E., Llinas, J., Hall, D.L.: Handbook of Multisensor Data Fusion: Theoryand Practice, 2nd edn. CRC Press, Boca Raton (2008)10. Mart´ı, E., Garc´ıa, J., Molina, J.: Opportunistic multisensor fusion for robust nav-igation in smart environments. In: Proceedings of CONTEXTS 2011 WorkshopUser-Centric Technologies and Applications, Springer, Heidelberg (2011)11. Nemra, A., Aouf, N.: Robust INS/GPS Sensor Fusion for UAV Localization UsingSDRE Nonlinear Filtering. IEEE Sensors Journal 10(4), 789–798 (2010)12. Subercaze, J., Maret, P., Dang, N.M., Sasaki, K.: Context-aware applications usingpersonal sensors. In: Proceedings of the ICST 2nd International Conference onBody Area Networks, p. 19 (June 2007)13. Wendel, J., Trommer, G.: Tightly coupled GPS/INS integration for missile appli-cations. Aerospace Science and Technology 8(7), 627–634 (2004)8

Enrique Martí

Jesús García

Jose Manuel Molina

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Context-Awareness at the Service of Sensor Fusion Systems: Inverting the Usual Scheme

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