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Mielle, M., Magnusson, M. & Lilienthal, A. (2019). A comparative analysis of radar and lidar sensing for localization and mapping. In: : . Paper presented at 9th European Conference on Mobile Robots (ECMR 2019), Prague, Czech Republic, September 4-6, 2019. IEEE
Open this publication in new window or tab >>A comparative analysis of radar and lidar sensing for localization and mapping
2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

Lidars and cameras are the sensors most commonly used for Simultaneous Localization And Mapping (SLAM). However, they are not effective in certain scenarios, e.g. when fire and smoke are present in the environment. While radars are much less affected by such conditions, radar and lidar have rarely been compared in terms of the achievable SLAM accuracy. We present a principled comparison of the accuracy of a novel radar sensor against that of a Velodyne lidar, for localization and mapping.

We evaluate the performance of both sensors by calculating the displacement in position and orientation relative to a ground-truth reference positioning system, over three experiments in an indoor lab environment. We use two different SLAM algorithms and found that the mean displacement in position when using the radar sensor was less than 0.037 m, compared to 0.011m for the lidar. We show that while producing slightly less accurate maps than a lidar, the radar can accurately perform SLAM and build a map of the environment, even including details such as corners and small walls.

Place, publisher, year, edition, pages
IEEE, 2019
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-76976 (URN)
Conference
9th European Conference on Mobile Robots (ECMR 2019), Prague, Czech Republic, September 4-6, 2019
Available from: 2019-10-02 Created: 2019-10-02 Last updated: 2019-10-02Bibliographically approved
Hüllmann, D., Neumann, P. P., Monroy, J. & Lilienthal, A. (2019). A Realistic Remote Gas Sensor Model for Three-Dimensional Olfaction Simulations. In: ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN): . Paper presented at 2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), Fukuoka, japan, mMy 26-29, 2019 (pp. 1-3). IEEE
Open this publication in new window or tab >>A Realistic Remote Gas Sensor Model for Three-Dimensional Olfaction Simulations
2019 (English)In: ISOCS/IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), IEEE, 2019, p. 1-3Conference paper, Published paper (Refereed)
Abstract [en]

Remote gas sensors like those based on the Tunable Diode Laser Absorption Spectroscopy (TDLAS) enable mobile robots to scan huge areas for gas concentrations in reasonable time and are therefore well suited for tasks such as gas emission surveillance and environmental monitoring. A further advantage of remote sensors is that the gas distribution is not disturbed by the sensing platform itself if the measurements are carried out from a sufficient distance, which is particularly interesting when a rotary-wing platform is used. Since there is no possibility to obtain ground truth measurements of gas distributions, simulations are used to develop and evaluate suitable olfaction algorithms. For this purpose several models of in-situ gas sensors have been developed, but models of remote gas sensors are missing. In this paper we present two novel 3D ray-tracer-based TDLAS sensor models. While the first model simplifies the laser beam as a line, the second model takes the conical shape of the beam into account. Using a simulated gas plume, we compare the line model with the cone model in terms of accuracy and computational cost and show that the results generated by the cone model can differ significantly from those of the line model.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
gas detector, remote gas sensor, sensor modelling, TDLAS, gas dispersion simulation
National Category
Remote Sensing Robotics
Identifiers
urn:nbn:se:oru:diva-76220 (URN)10.1109/ISOEN.2019.8823330 (DOI)978-1-5386-8327-9 (ISBN)978-1-5386-8328-6 (ISBN)
Conference
2019 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), Fukuoka, japan, mMy 26-29, 2019
Available from: 2019-09-11 Created: 2019-09-11 Last updated: 2019-09-11Bibliographically approved
Neumann, P. P., Hüllmann, D., Hüllmann, D., Krentel, D., Kluge, M., Dzierliński, M., . . . Bartholmai, M. (2019). Aerial-based gas tomography: from single beams to complex gas distributions. European Journal of Remote Sensing, 1-15
Open this publication in new window or tab >>Aerial-based gas tomography: from single beams to complex gas distributions
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2019 (English)In: European Journal of Remote Sensing, ISSN 2279-7254, p. 1-15Article in journal (Refereed) Epub ahead of print
Abstract [en]

In this paper, we present and validate the concept of an autonomous aerial robot to reconstruct tomographic 2D slices of gas plumes in outdoor environments. Our platform, the so-called Unmanned Aerial Vehicle for Remote Gas Sensing (UAV-REGAS), combines a lightweight Tunable Diode Laser Absorption Spectroscopy (TDLAS) gas sensor with a 3-axis aerial stabilization gimbal for aiming at a versatile octocopter. While the TDLAS sensor provides integral gas concentration measurements, it does not measure the distance traveled by the laser diode?s beam nor the distribution of gas along the optical path. Thus, we complement the set-up with a laser rangefinder and apply principles of Computed Tomography (CT) to create a model of the spatial gas distribution from a set of integral concentration measurements. To allow for a fundamental ground truth evaluation of the applied gas tomography algorithm, we set up a unique outdoor test environment based on two 3D ultrasonic anemometers and a distributed array of 10 infrared gas transmitters. We present results showing its performance characteristics and 2D plume reconstruction capabilities under realistic conditions. The proposed system can be deployed in scenarios that cannot be addressed by currently available robots and thus constitutes a significant step forward for the field of Mobile Robot Olfaction (MRO).

Place, publisher, year, edition, pages
London: Taylor & Francis, 2019
Keywords
Aerial robot olfaction, mobile robot olfaction, gas tomography, TDLAS, plume
National Category
Remote Sensing Occupational Health and Environmental Health Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-76009 (URN)10.1080/22797254.2019.1640078 (DOI)
Available from: 2019-09-02 Created: 2019-09-02 Last updated: 2019-09-06Bibliographically approved
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: 2019-06-19Bibliographically approved
Schindler, M. & Lilienthal, A. J. (2019). Domain-specific interpretation of eye tracking data: towards a refined use of the eye-mind hypothesis for the field of geometry. Educational Studies in Mathematics, 101(1), 123-139
Open this publication in new window or tab >>Domain-specific interpretation of eye tracking data: towards a refined use of the eye-mind hypothesis for the field of geometry
2019 (English)In: Educational Studies in Mathematics, ISSN 0013-1954, E-ISSN 1573-0816, Vol. 101, no 1, p. 123-139Article in journal (Refereed) Published
Abstract [en]

Eye tracking is getting increasingly popular in mathematics education research. Studies predominantly rely on the so-called eye-mind hypothesis (EMH), which posits that what persons fixate on closely relates to what they process. Given that the EMH was developed in reading research, we see the risk that implicit assumptions are tacitly adopted in mathematics even though they may not apply in this domain. This article investigates to what extent the EMH applies in mathematics - geometry in particular - and aims to lift the discussion of what inferences can be validly made from eye-tracking data. We use a case study to investigate the need for a refinement of the use of the EMH. In a stimulated recall interview, a student described his original thoughts perusing a gaze-overlaid video recorded when he was working on a geometry problem. Our findings contribute to better a understanding of when and how the EMH applies in the subdomain of geometry. In particular, we identify patterns of eye movements that provide valuable information on students' geometry problem solving: certain patterns where the eye fixates on what the student is processing and others where the EMH does not hold. Identifying such patterns may contribute to an interpretation theory for students' eye movements in geometry - exemplifying a domain-specific theory that may reduce the inherent ambiguity and uncertainty that eye tracking data analysis has.

Place, publisher, year, edition, pages
Springer, 2019
Keywords
Eye tracking, Eye movements, Eye-mind hypothesis, Geometry
National Category
Educational Sciences
Identifiers
urn:nbn:se:oru:diva-73868 (URN)10.1007/s10649-019-9878-z (DOI)000463669800009 ()2-s2.0-85061182709 (Scopus ID)
Available from: 2019-04-23 Created: 2019-04-23 Last updated: 2019-04-23Bibliographically approved
Hüllmann, D., Neumann, P. P. & Lilienthal, A. (2019). Gas Dispersion Fluid Mechanics Simulation for Large Outdoor Environments. In: 36th Danubia Adria Symposium on Advances in Experimental Mechanics: Extended Abstracts. Paper presented at 36th Danubia Adria Symposium on Advances in Experimental Mechanics, Plzeň, Czech Republic, 24–27 September 2019 (pp. 49-50). Pilsen, Czech Republic: Danubia-Adria Symposium on Advances in Experimental Mechanics
Open this publication in new window or tab >>Gas Dispersion Fluid Mechanics Simulation for Large Outdoor Environments
2019 (English)In: 36th Danubia Adria Symposium on Advances in Experimental Mechanics: Extended Abstracts, Pilsen, Czech Republic: Danubia-Adria Symposium on Advances in Experimental Mechanics , 2019, p. 49-50Conference paper, Poster (with or without abstract) (Refereed)
Abstract [en]

The development of algorithms for mapping gas distributions and localising gas sources is a challenging task, because gas dispersion is a highly dynamic process and it is impossible to capture ground truth data. Fluid-mechanical simulations are a suitable way to support the development of these algorithms. Several tools for gas dispersion simulation have been developed, but they are not suitable for simulations of large outdoor environments. In this paper, we present a concept of how an existing simulator can be extended to handle both indoor and large outdoor scenarios.

Place, publisher, year, edition, pages
Pilsen, Czech Republic: Danubia-Adria Symposium on Advances in Experimental Mechanics, 2019
Keywords
Gas dispersion simulation, CFD, gas tomography
National Category
Robotics Remote Sensing Fluid Mechanics and Acoustics
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-77198 (URN)978-80-261-0876-4 (ISBN)
Conference
36th Danubia Adria Symposium on Advances in Experimental Mechanics, Plzeň, Czech Republic, 24–27 September 2019
Available from: 2019-10-11 Created: 2019-10-11 Last updated: 2019-10-15Bibliographically approved
Wiedemann, T., Shutin, D. & Lilienthal, A. (2019). Model-based gas source localization strategy for a cooperative multi-robot system-A probabilistic approach and experimental validation incorporating physical knowledge and model uncertainties. Robotics and Autonomous Systems, 118, 66-79
Open this publication in new window or tab >>Model-based gas source localization strategy for a cooperative multi-robot system-A probabilistic approach and experimental validation incorporating physical knowledge and model uncertainties
2019 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 118, p. 66-79Article in journal (Refereed) Published
Abstract [en]

Sampling gas distributions by robotic platforms in order to find gas sources is an appealing approach to alleviate threats for a human operator. Different sampling strategies for robotic gas exploration exist. In this paper we investigate the benefit that could be obtained by incorporating physical knowledge about the gas dispersion. By exploring a gas diffusion process using a multi-robot system. The physical behavior of the diffusion process is modeled using a Partial Differential Equation (PDE) which is integrated into the exploration strategy. It is assumed that the diffusion process is driven by only a few spatial sources at unknown locations with unknown intensity. The objective of the exploration strategy is to guide the robots to informative measurement locations and by means of concentration measurements estimate the source parameters, in particular, their number, locations and magnitudes. To this end we propose a probabilistic approach towards PDE identification under sparsity constraints using factor graphs and a message passing algorithm. Moreover, message passing schemes permit efficient distributed implementation of the algorithm, which makes it suitable for a multi-robot system. We designed an experimental setup that allows us to evaluate the performance of the exploration strategy in hardware-in-the-loop experiments as well as in experiments with real ethanol gas under laboratory conditions. The results indicate that the proposed exploration approach accelerates the identification of the source parameters and outperforms systematic sampling. (C) 2019 Elsevier B.V. All rights reserved.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Robotic exploration, Gas source localization, Multi-agent-system, Partial differential equation, Mobile robot olfaction, Sparse Bayesian learning, Factor graph, Message passing
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
urn:nbn:se:oru:diva-75365 (URN)10.1016/j.robot.2019.03.014 (DOI)000474324100006 ()2-s2.0-85065544153 (Scopus ID)
Funder
EU, European Research Council, 645101
Available from: 2019-07-29 Created: 2019-07-29 Last updated: 2019-07-29Bibliographically 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)000462540400138 ()30841615 (PubMedID)2-s2.0-85062613532 (Scopus ID)
Funder
Vinnova, 2017-05468
Available from: 2019-03-18 Created: 2019-03-18 Last updated: 2019-06-19Bibliographically approved
Fan, H., Hernandez Bennetts, V., Schaffernicht, E. & Lilienthal, A. (2019). Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers. In: ISOEN 2019: 18th International Symposium on Olfaction and Electronic Nose. Paper presented at 2019 IEEE 18th International Symposium on Olfaction and Electronic Nose (ISOEN), Fukoka, Japan, May 26-29, 2019. IEEE, Article ID 151773.
Open this publication in new window or tab >>Semi-supervised Gas Detection Using an Ensemble of One-class Classifiers
2019 (English)In: ISOEN 2019: 18th International Symposium on Olfaction and Electronic Nose, IEEE, 2019, article id 151773Conference paper, Published paper (Refereed)
Abstract [en]

Detecting chemical compounds using electronic noses is important in many gas sensing related applications. Existing gas detection methods typically use prior knowledge of the target analytes. However, in some scenarios, the analytes to be detected are not fully known in advance, and preparing a dedicated model is not possible. To address this issue, we propose a gas detection approach using an ensemble of one-class classifiers. The proposed approach is initialized by learning a Mahalanobis-based and a Gaussian based model using clean air only. During the sampling process, the presence of chemicals is detected by the initialized system, which allows to learn a one-class nearest neighbourhood model without supervision. From then on the gas detection considers the predictions of the three one-class models. The proposed approach is validated with real-world experiments, in which a mobile robot equipped with an e-nose was remotely controlled to interact with different chemical analytes in an open environment.

Place, publisher, year, edition, pages
IEEE, 2019
Keywords
Metal oxide semiconductor sensor, electronic nose, gas detection, gas sensing, open sampling systems
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:oru:diva-77210 (URN)10.1109/ISOEN.2019.8823148 (DOI)2-s2.0-85072989108 (Scopus ID)
Conference
2019 IEEE 18th International Symposium on Olfaction and Electronic Nose (ISOEN), Fukoka, Japan, May 26-29, 2019
Projects
SmokeBot
Available from: 2019-10-13 Created: 2019-10-13 Last updated: 2019-10-15Bibliographically 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: 2019-06-19Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-0217-9326

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