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Wiedemann, T., Lilienthal, A. & Shutin, D. (2019). Analysis of Model Mismatch Effects for a Model-based Gas Source Localization Strategy Incorporating Advection Knowledge. Sensors, 19(3), Article ID 520.
Open this publication in new window or tab >>Analysis of Model Mismatch Effects for a Model-based Gas Source Localization Strategy Incorporating Advection Knowledge
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 3, article id 520Article in journal (Refereed) Published
Abstract [en]

In disaster scenarios, where toxic material is leaking, gas source localization is a common but also dangerous task. To reduce threats for human operators, we propose an intelligent sampling strategy that enables a multi-robot system to autonomously localize unknown gas sources based on gas concentration measurements. This paper discusses a probabilistic, model-based approach for incorporating physical process knowledge into the sampling strategy. We model the spatial and temporal dynamics of the gas dispersion with a partial differential equation that accounts for diffusion and advection effects. We consider the exact number of sources as unknown, but assume that gas sources are sparsely distributed. To incorporate the sparsity assumption we make use of sparse Bayesian learning techniques. Probabilistic modeling can account for possible model mismatch effects that otherwise can undermine the performance of deterministic methods. In the paper we evaluate the proposed gas source localization strategy in simulations using synthetic data. Compared to real-world experiments, a simulated environment provides us with ground truth data and reproducibility necessary to get a deeper insight into the proposed strategy. The investigation shows that (i) the probabilistic model can compensate imperfect modeling; (ii) the sparsity assumption significantly accelerates the source localization; and (iii) a-priori advection knowledge is of advantage for source localization, however, it is only required to have a certain level of accuracy. These findings will help in the future to parameterize the proposed algorithm in real world applications.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2019
Keywords
Robotic exploration, gas source localization, mobile robot olfaction, sparse Bayesian learning, multi-agent system, advection-diffusion model
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71964 (URN)10.3390/s19030520 (DOI)000459941200083 ()30691174 (PubMedID)2-s2.0-85060572534 (Scopus ID)
Projects
SmokeBot (EC H2020, 645101)
Note

Funding Agencies:

European Commission  645101 

Valles Marineris Explorer initiative of DLR (German Aerospace Center) Space Administration 

Available from: 2019-01-31 Created: 2019-01-31 Last updated: 2019-03-19Bibliographically approved
Hernandez Bennetts, V., Kamarudin, K., Wiedemann, T., Kucner, T. P., Somisetty, S. L. & Lilienthal, A. (2019). Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning. Sensors, 19(5), Article ID E1119.
Open this publication in new window or tab >>Multi-Domain Airflow Modeling and Ventilation Characterization Using Mobile Robots, Stationary Sensors and Machine Learning
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2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 5, article id E1119Article in journal (Refereed) Published
Abstract [en]

Ventilation systems are critically important components of many public buildings and workspaces. Proper ventilation is often crucial for preventing accidents, such as explosions in mines and avoiding health issues, for example, through long-term exposure to harmful respirable matter. Validation and maintenance of ventilation systems is thus of key interest for plant operators and authorities. However, methods for ventilation characterization, which allow us to monitor whether the ventilation system in place works as desired, hardly exist. This article addresses the critical challenge of ventilation characterization-measuring and modelling air flow at micro-scales-that is, creating a high-resolution model of wind speed and direction from airflow measurements. Models of the near-surface micro-scale flow fields are not only useful for ventilation characterization, but they also provide critical information for planning energy-efficient paths for aerial robots and many applications in mobile robot olfaction. In this article we propose a heterogeneous measurement system composed of static, continuously sampling sensing nodes, complemented by localized measurements, collected during occasional sensing missions with a mobile robot. We introduce a novel, data-driven, multi-domain airflow modelling algorithm that estimates (1) fields of posterior distributions over wind direction and speed ("ventilation maps", spatial domain); (2) sets of ventilation calendars that capture the evolution of important airflow characteristics at measurement positions (temporal domain); and (3) a frequency domain analysis that can reveal periodic changes of airflow in the environment. The ventilation map and the ventilation calendars make use of an improved estimation pipeline that incorporates a wind sensor model and a transition model to better filter out sporadic, noisy airflow changes. These sudden changes may originate from turbulence or irregular activity in the surveyed environment and can, therefore, disturb modelling of the relevant airflow patterns. We tested the proposed multi-domain airflow modelling approach with simulated data and with experiments in a semi-controlled environment and present results that verify the accuracy of our approach and its sensitivity to different turbulence levels and other disturbances. Finally, we deployed the proposed system in two different real-world industrial environments (foundry halls) with different ventilation regimes for three weeks during full operation. Since airflow ground truth cannot be obtained, we present a qualitative discussion of the generated airflow models with plant operators, who concluded that the computed models accurately depicted the expected airflow patterns and are useful to understand how pollutants spread in the work environment. This analysis may then provide the basis for decisions about corrective actions to avoid long-term exposure of workers to harmful respirable matter.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
Airflow modeling, environmental monitoring, machine learning, mobile robotics, static sensor networks, ventilation
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-73199 (URN)10.3390/s19051119 (DOI)30841615 (PubMedID)2-s2.0-85062613532 (Scopus ID)
Available from: 2019-03-18 Created: 2019-03-18 Last updated: 2019-03-18Bibliographically approved
Burgués, J., Hernandez Bennetts, V., Lilienthal, A. & Marco, S. (2019). Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping. Sensors, 19(3), Article ID 478.
Open this publication in new window or tab >>Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 3, article id 478Article in journal (Refereed) Published
Abstract [en]

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the ‘bout’ detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m2) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2019
Keywords
Robotics, signal processing, electronics, gas source localization, gas distribution mapping; gas sensors, drone, UAV, MOX sensor, quadcopter
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71963 (URN)10.3390/s19030478 (DOI)000459941200041 ()30682827 (PubMedID)2-s2.0-85060510907 (Scopus ID)
Note

Funding Agency:

Spanish MINECO  BES-2015-071698  TEC2014-59229-R

Available from: 2019-01-31 Created: 2019-01-31 Last updated: 2019-03-19Bibliographically approved
Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. (2019). Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose. Sensors, 19(3), Article ID E685.
Open this publication in new window or tab >>Towards Gas Discrimination and Mapping in Emergency Response Scenarios Using a Mobile Robot with an Electronic Nose
2019 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 3, article id E685Article in journal (Refereed) Published
Abstract [en]

Emergency personnel, such as firefighters, bomb technicians, and urban search and rescue specialists, can be exposed to a variety of extreme hazards during the response to natural and human-made disasters. In many of these scenarios, a risk factor is the presence of hazardous airborne chemicals. The recent and rapid advances in robotics and sensor technologies allow emergency responders to deal with such hazards from relatively safe distances. Mobile robots with gas-sensing capabilities allow to convey useful information such as the possible source positions of different chemicals in the emergency area. However, common gas sampling procedures for laboratory use are not applicable due to the complexity of the environment and the need for fast deployment and analysis. In addition, conventional gas identification approaches, based on supervised learning, cannot handle situations when the number and identities of the present chemicals are unknown. For the purpose of emergency response, all the information concluded from the gas detection events during the robot exploration should be delivered in real time. To address these challenges, we developed an online gas-sensing system using an electronic nose. Our system can automatically perform unsupervised learning and update the discrimination model as the robot is exploring a given environment. The online gas discrimination results are further integrated with geometrical information to derive a multi-compound gas spatial distribution map. The proposed system is deployed on a robot built to operate in harsh environments for supporting fire brigades, and is validated in several different real-world experiments of discriminating and mapping multiple chemical compounds in an indoor open environment. Our results show that the proposed system achieves high accuracy in gas discrimination in an online, unsupervised, and computationally efficient manner. The subsequently created gas distribution maps accurately indicate the presence of different chemicals in the environment, which is of practical significance for emergency response.

Place, publisher, year, edition, pages
MDPI, 2019
Keywords
Emergency response, gas discrimination, gas distribution mapping, mobile robotics olfaction, search and rescue robot, unsupervised learning
National Category
Robotics
Identifiers
urn:nbn:se:oru:diva-72366 (URN)10.3390/s19030685 (DOI)000459941200248 ()30736489 (PubMedID)2-s2.0-85061226919 (Scopus ID)
Note

Funding Agency:

European Commission  645101

Available from: 2019-02-12 Created: 2019-02-12 Last updated: 2019-03-19Bibliographically approved
Fan, H., Lu, D., Kucner, T. P., Magnusson, M. & Lilienthal, A. (2018). 2D Spatial Keystone Transform for Sub-Pixel Motion Extraction from Noisy Occupancy Grid Map. In: Proceedings of 21st International Conference on Information Fusion (FUSION): . Paper presented at 21st International Conference on Information Fusion (FUSION), Cambridge, UK, July 10 - 13, 2018 (pp. 2400-2406).
Open this publication in new window or tab >>2D Spatial Keystone Transform for Sub-Pixel Motion Extraction from Noisy Occupancy Grid Map
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2018 (English)In: Proceedings of 21st International Conference on Information Fusion (FUSION), 2018, p. 2400-2406Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, we propose a novel sub-pixel motionextraction method, called as Two Dimensional Spatial KeystoneTransform (2DS-KST), for the motion detection and estimationfrom successive noisy Occupancy Grid Maps (OGMs). It extendsthe KST in radar imaging or motion compensation to 2Dreal spatial case, based on multiple hypotheses about possibledirections of moving obstacles. Simulation results show that 2DSKSThas a good performance on the extraction of sub-pixelmotions in very noisy environment, especially for those slowlymoving obstacles.

Keywords
robotics, occupancy grid map, motion extraction, keystone transform, 2DS-KST, sub-pixel
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71953 (URN)10.23919/ICIF.2018.8455274 (DOI)978-0-9964527-6-2 (ISBN)978-1-5386-4330-3 (ISBN)
Conference
21st International Conference on Information Fusion (FUSION), Cambridge, UK, July 10 - 13, 2018
Available from: 2019-01-30 Created: 2019-01-30 Last updated: 2019-02-01Bibliographically approved
Burgués, J., Hernandez Bennetts, V., Lilienthal, A. & Marco, S. (2018). 3D Gas Distribution with and without Artificial Airflow: An Experimental Study with a Grid of Metal Oxide Semiconductor Gas Sensors. Proceedings, 2(13), Article ID 911.
Open this publication in new window or tab >>3D Gas Distribution with and without Artificial Airflow: An Experimental Study with a Grid of Metal Oxide Semiconductor Gas Sensors
2018 (English)In: Proceedings, E-ISSN 2504-3900, Vol. 2, no 13, article id 911Article in journal (Refereed) Published
Abstract [en]

Gas distribution modelling can provide potentially life-saving information when assessing the hazards of gaseous emissions and for localization of explosives, toxic or flammable chemicals. In this work, we deployed a three-dimensional (3D) grid of metal oxide semiconductor (MOX) gas sensors deployed in an office room, which allows for novel insights about the complex patterns of indoor gas dispersal. 12 independent experiments were carried out to better understand dispersion patters of a single gas source placed at different locations of the room, including variations in height, release rate and air flow profiles. This dataset is denser and richer than what is currently available, i.e., 2D datasets in wind tunnels. We make it publicly available to enable the community to develop, validate, and compare new approaches related to gas sensing in complex environments.

Place, publisher, year, edition, pages
Basel, Switzerland: MDPI, 2018
Keywords
MOX, metal oxide, flow visualization, gas sensors, gas distribution mapping, sensor grid, 3D, gas source localization, indoor
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-71962 (URN)10.3390/proceedings2130911 (DOI)
Projects
SmokeBot (EC H2020, 645101)
Available from: 2019-01-31 Created: 2019-01-31 Last updated: 2019-02-01Bibliographically approved
Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. (2018). A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments. Sensors and actuators. B, Chemical, 259, 183-203
Open this publication in new window or tab >>A cluster analysis approach based on exploiting density peaks for gas discrimination with electronic noses in open environments
2018 (English)In: Sensors and actuators. B, Chemical, ISSN 0925-4005, E-ISSN 1873-3077, Vol. 259, p. 183-203Article in journal (Refereed) Published
Abstract [en]

Gas discrimination in open and uncontrolled environments based on smart low-cost electro-chemical sensor arrays (e-noses) is of great interest in several applications, such as exploration of hazardous areas, environmental monitoring, and industrial surveillance. Gas discrimination for e-noses is usually based on supervised pattern recognition techniques. However, the difficulty and high cost of obtaining extensive and representative labeled training data limits the applicability of supervised learning. Thus, to deal with the lack of information regarding target substances and unknown interferents, unsupervised gas discrimination is an advantageous solution. In this work, we present a cluster-based approach that can infer the number of different chemical compounds, and provide a probabilistic representation of the class labels for the acquired measurements in a given environment. Our approach is validated with the samples collected in indoor and outdoor environments using a mobile robot equipped with an array of commercial metal oxide sensors. Additional validation is carried out using a multi-compound data set collected with stationary sensor arrays inside a wind tunnel under various airflow conditions. The results show that accurate class separation can be achieved with a low sensitivity to the selection of the only free parameter, namely the neighborhood size, which is used for density estimation in the clustering process.

Place, publisher, year, edition, pages
Amsterda, Netherlands: Elsevier, 2018
Keywords
Gas discrimination, environmental monitoring, metal oxide sensors, cluster analysis, unsupervised learning
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-63468 (URN)10.1016/j.snb.2017.10.063 (DOI)000424877600023 ()2-s2.0-85038032167 (Scopus ID)
Projects
SmokBot
Funder
EU, Horizon 2020, 645101
Available from: 2017-12-19 Created: 2017-12-19 Last updated: 2019-02-12Bibliographically approved
Fan, H., Kucner, T. P., Magnusson, M., Li, T. & Lilienthal, A. (2018). A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment. IEEE transactions on intelligent transportation systems (Print), 19(9), 2977-2993
Open this publication in new window or tab >>A Dual PHD Filter for Effective Occupancy Filtering in a Highly Dynamic Environment
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2018 (English)In: IEEE transactions on intelligent transportation systems (Print), ISSN 1524-9050, E-ISSN 1558-0016, Vol. 19, no 9, p. 2977-2993Article in journal (Refereed) Published
Abstract [en]

Environment monitoring remains a major challenge for mobile robots, especially in densely cluttered or highly populated dynamic environments, where uncertainties originated from environment and sensor significantly challenge the robot's perception. This paper proposes an effective occupancy filtering method called the dual probability hypothesis density (DPHD) filter, which models uncertain phenomena, such as births, deaths, occlusions, false alarms, and miss detections, by using random finite sets. The key insight of our method lies in the connection of the idea of dynamic occupancy with the concepts of the phase space density in gas kinetic and the PHD in multiple target tracking. By modeling the environment as a mixture of static and dynamic parts, the DPHD filter separates the dynamic part from the static one with a unified filtering process, but has a higher computational efficiency than existing Bayesian Occupancy Filters (BOFs). Moreover, an adaptive newborn function and a detection model considering occlusions are proposed to improve the filtering efficiency further. Finally, a hybrid particle implementation of the DPHD filter is proposed, which uses a box particle filter with constant discrete states and an ordinary particle filter with a time-varying number of particles in a continuous state space to process the static part and the dynamic part, respectively. This filter has a linear complexity with respect to the number of grid cells occupied by dynamic obstacles. Real-world experiments on data collected by a lidar at a busy roundabout demonstrate that our approach can handle monitoring of a highly dynamic environment in real time.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2018
Keywords
Mobile robot, occupancy filtering, PHD filter, BOF, particle filter, random finite set
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-63981 (URN)10.1109/TITS.2017.2770152 (DOI)000444611400021 ()2-s2.0-85038368968 (Scopus ID)
Note

Funding Agencies:

EU Project SPENCER  600877 

Marie Sklodowska-Curie Individual Fellowship  709267 

National Twelfth Five-Year Plan for Science and Technology Support of China  2014BAK12B03 

Available from: 2018-01-09 Created: 2018-01-09 Last updated: 2018-09-28Bibliographically approved
Mielle, M., Magnusson, M. & Lilienthal, A. J. (2018). A method to segment maps from different modalities using free space layout MAORIS: map of ripples segmentation. In: : . Paper presented at IEEE International Conference on Robotics and Automation (ICRA 2018), Brisbane, Australia, May 21-25, 2018 (pp. 4993-4999). IEEE Computer Society
Open this publication in new window or tab >>A method to segment maps from different modalities using free space layout MAORIS: map of ripples segmentation
2018 (English)Conference paper, Published paper (Refereed)
Abstract [en]

How to divide floor plans or navigation maps into semantic representations, such as rooms and corridors, is an important research question in fields such as human-robot interaction, place categorization, or semantic mapping. While most works focus on segmenting robot built maps, those are not the only types of map a robot, or its user, can use. We present a method for segmenting maps from different modalities, focusing on robot built maps and hand-drawn sketch maps, and show better results than state of the art for both types.

Our method segments the map by doing a convolution between the distance image of the map and a circular kernel, and grouping pixels of the same value. Segmentation is done by detecting ripple-like patterns where pixel values vary quickly, and merging neighboring regions with similar values.

We identify a flaw in the segmentation evaluation metric used in recent works and propose a metric based on Matthews correlation coefficient (MCC). We compare our results to ground-truth segmentations of maps from a publicly available dataset, on which we obtain a better MCC than the state of the art with 0.98 compared to 0.65 for a recent Voronoi-based segmentation method and 0.70 for the DuDe segmentation method.

We also provide a dataset of sketches of an indoor environment, with two possible sets of ground truth segmentations, on which our method obtains an MCC of 0.56 against 0.28 for the Voronoi-based segmentation method and 0.30 for DuDe.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Keywords
map segmentation, free space, layout
National Category
Robotics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-68421 (URN)000446394503114 ()
Conference
IEEE International Conference on Robotics and Automation (ICRA 2018), Brisbane, Australia, May 21-25, 2018
Funder
EU, Horizon 2020, ICT-23-2014 645101 SmokeBotKnowledge Foundation, 20140220
Available from: 2018-08-09 Created: 2018-08-09 Last updated: 2018-10-22Bibliographically approved
Canelhas, D. R., Stoyanov, T. & Lilienthal, A. J. (2018). A Survey of Voxel Interpolation Methods and an Evaluation of Their Impact on Volumetric Map-Based Visual Odometry. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA),: . Paper presented at IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, May 21-25, 2018 (pp. 6337-6343). IEEE Computer Society
Open this publication in new window or tab >>A Survey of Voxel Interpolation Methods and an Evaluation of Their Impact on Volumetric Map-Based Visual Odometry
2018 (English)In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA),, IEEE Computer Society, 2018, p. 6337-6343Conference paper, Published paper (Refereed)
Abstract [en]

Voxel volumes are simple to implement and lend themselves to many of the tools and algorithms available for 2D images. However, the additional dimension of voxels may be costly to manage in memory when mapping large spaces at high resolutions. While lowering the resolution and using interpolation is common work-around, in the literature we often find that authors either use trilinear interpolation or nearest neighbors and rarely any of the intermediate options. This paper presents a survey of geometric interpolation methods for voxel-based map representations. In particular we study the truncated signed distance field (TSDF) and the impact of using fewer than 8 samples to perform interpolation within a depth-camera pose tracking and mapping scenario. We find that lowering the number of samples fetched to perform the interpolation results in performance similar to the commonly used trilinear interpolation method, but leads to higher framerates. We also report that lower bit-depth generally leads to performance degradation, though not as much as may be expected, with voxels containing as few as 3 bits sometimes resulting in adequate estimation of camera trajectories.

Place, publisher, year, edition, pages
IEEE Computer Society, 2018
Keywords
Voxels, Compression, Interpolation, TSDF, Visual Odometry
National Category
Robotics Computer Systems
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-67850 (URN)000446394504116 ()
Conference
IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia, May 21-25, 2018
Projects
H2020 ILIADH2020 Roblog
Funder
EU, Horizon 2020, 732737
Available from: 2018-07-11 Created: 2018-07-11 Last updated: 2018-10-22Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0217-9326

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