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Simultaneous Localization and Mapping using Range Only Sensors

Thesis
F. Herranz
Phd. Thesis Dissertation, University of Alcalá, Sept 2013

Abstract

Mobile robotics has become a popular topic in robotics due to the large number of service problems that can solve. Due to this fact, the demand for autonomous navigation and guidance applications has been increased in the last decades. These applications are based on the localization and mapping. Localization problem is solved using maps and sensor information of the environment. On the contrary, mapping problem is solved using location and sensor information. By means of the information provided by range only sensors and probabilistic techniques, it is possible to obtain beacon maps of environment and estimate the user location. Nowadays, range only sensors are low-cost, non-intrusive sensors and provide an unique identifier per beacon but they do not provide any angle information which makes it difficult localization and mapping.

In outdoor environments, navigation is a  well managed problem by global positioning system. However, it is generally not suitable to establish indoor localization and mapping systems, since microwaves will be attenuated and scattered by roofs, walls and other objects.

In indoor environments an accurate observation model is needed to provide a good relationship between signal level and distance. This thesis aims at obtaining a generic observation model of WiFi signal propagation.

In this thesis several algorithms are proposed in order to obtain a range only-based localization from a prior beacon map. For that purpose, it is necessary to compute a beacon map of the environment previously. Lateration techniques are presented as simple solutions to the localization problem. However, their performances are affected by geometry problems and the noise that affects the range only measurements. On the contrary, probabilistic methods such as particle filter are able to manage the characteristic of range only sensors and provide an accurate localization.

The map can be built using mapping techniques which are tested in this thesis. Mapping must deal with the lack of angle information of range only sensors which makes it impossible to estimate the beacon location with only one sample. Particle filter has been presented as a good choice to map the environment when having an accurate location of the robot. For computing the map when the location of the robot is inaccurate, this thesis employs Simultaneous Localization and Mapping techniques with a robot that obtains range and odometry information. Several techniques such as filtering and smoothing approaches are tested and validated.

Finally, the best combination of mapping and localization techniques (smoothing and particle filter) are used in a real application to provide an effective collaboration of human-robot teams within ABSYNTHE project.

Mapping Based on a Noisy Range-Only Sensor

Journal paper
F. Herranz, M. Ocaña, L. M. Bergasa, N. Hernández, Á. Llamazares, C. Fernández
Computer Aided Systems Theory – EUROCAST 2011 Lecture Notes in Computer Science Volume 6928, 2012, pp 420-425, Springer Berlin Heidelberg.

Abstract

Mapping techniques based on Wireless Range-Only Sensors (WROS) consist of locating the beacons using measurements of distance only. In this work we use WROS working at 2.4GHz band (same as WiFi, Wireless Fidelity), which has the disadvantage of being affected by a high noise. The goal of this paper is to study a noisy range-only sensor and its application in the development of mapping systems. A particle filter is used in order to map the environment, this technique has been applied successfully with other technologies, like Ultra-Wide Band (UWB), but we demonstrate that even using a noisier sensor this technique can be applied correctly.


BibTex:

@incollection{
year={2012},
isbn={978-3-642-27578-4},
booktitle={Computer Aided Systems Theory – EUROCAST 2011},
volume={6928},
series={Lecture Notes in Computer Science},
editor={Moreno-Díaz, Roberto and Pichler, Franz and Quesada-Arencibia, Alexis},
doi={10.1007/978-3-642-27579-1_54},
title={Mapping Based on a Noisy Range-Only Sensor},
url={http://dx.doi.org/10.1007/978-3-642-27579-1_54},
publisher={Springer Berlin Heidelberg},
author={Herranz, F. and Ocaña, M. and Bergasa, L.M. and Hernández, N. and Llamazares, A. and Fernández, C.},
pages={420-425},
language={English}
}

3D Map Building Using a 2D Laser Scanner

Journal paper
Á. Llamazares, E. J. Molinos, M. Ocaña, L. M. Bergasa, N. Hernández, F. Herranz
Computer Aided Systems Theory – EUROCAST 2011 Lecture Notes in Computer Science Volume 6928, 2012, pp 412-419, Springer Berlin Heidelberg

Abstract

In this paper we present a technique to build 3D maps of the environment using a 2D laser scanner combined with a robot’s action model. This paper demonstrates that it is possible to build 3D maps in a cheap way using an angled 2D laser. We introduce a scan matching method to minimize the odometer errors of the robotics platform and a calibration method to improve the accuracy of the system. Some experimental results and conclusions are presented.


 BibTex:

@incollection{
year={2012},
isbn={978-3-642-27578-4},
booktitle={Computer Aided Systems Theory – EUROCAST 2011},
volume={6928},
series={Lecture Notes in Computer Science},
editor={Moreno-Díaz, Roberto and Pichler, Franz and Quesada-Arencibia, Alexis},
doi={10.1007/978-3-642-27579-1_53},
title={3D Map Building Using a 2D Laser Scanner},
url={http://dx.doi.org/10.1007/978-3-642-27579-1_53},
publisher={Springer Berlin Heidelberg},
author={Llamazares, Á. and Molinos, E.J. and Ocaña, M. and Bergasa, L.M. and Hernández, N. and Herranz, F.},
pages={412-419},
language={English}
}

Enhanced WiFi localization system based on Soft Computing techniques to deal with small-scale variations in wireless sensors

Journal paper
J. Alonso, M. Ocaña, N. Hernández, F. Herranz, Á. Llamazares, M.A. Sotelo, L.M. Bergasa, L. Magdalena
Applied Soft Computing, Volume 11, Issue 8, December 2011, Pages 4677-4691, ISSN 1568-4946

Abstract

The framework of this paper is robot localization inside buildings by means of wireless localization systems. Such kind of systems make use of the Wireless Fidelity (WiFi) signal strength sensors which are becoming more and more useful in the localization stage of several robotic platforms. Robot localization is usually made up of two phases: training and estimation stages. In the former, WiFi signal strength of all visible Access Points (APs) are collected and stored in a database or WiFi map. In the latter, the signal strengths received from all APs at a certain position are compared with the WiFi map to estimate the robot location. Hence, WiFi localization systems exploit the well-known path loss propagation model due to large-scale variations of WiFi signal to determine how closer the robot is to a certain AP. Unfortunately, there is another kind of signal variations called small-scale variations that have to be considered. They appear when robots move under the wavelength λ. In consequence, a chaotic noise is added to the signal strength measure yielding a lot of uncertainty that should be handled by the localization model. While lateral and orientation errors in the robot positioning stage are well studied and they remain under control thanks to the use of robust low-level controllers, more studies are needed when dealing with small-scale variations. Moreover, if the robot can not use a robust low-level controller because, for example, the environment is not organized in perpendicular corridors, then lateral and orientation errors can be significantly increased yielding a bad global localization and navigation performance. The main goal of this work is to strengthen the localization stage of our previous WiFi Partially Observable Markov Decision Process (POMDP) Navigation System with the aim of dealing effectively with small-scale variations. In addition, looking for the applicability of our system to a wider variety of environments, we relax the necessity of having a robust low-level controller. To do that, this paper proposes the use of a Soft Computing based system to tackle with the uncertainty related to both the small-scale variations and the lack of a robust low-level controller. The proposed system is actually implemented in the form of a Fuzzy Rule-based System and it has been evaluated in two real test-beds and robotic platforms. Experimental results show how our system is easily adaptable to new environments where classical localization techniques can not be applied since the AP physical location is unknown.


 

BibTex:

@article{Alonso20114677,
title = “Enhanced WiFi localization system based on Soft Computing techniques to deal with small-scale variations in wireless sensors “,
journal = “Applied Soft Computing “,
volume = “11”,
number = “8”,
pages = “4677 – 4691″,
year = “2011”,
note = “”,
issn = “1568-4946″,
doi = “http://dx.doi.org/10.1016/j.asoc.2011.07.015″,
url = “http://www.sciencedirect.com/science/article/pii/S1568494611002766″,
author = “Jose M. Alonso and Manuel Ocaña and Noelia Hernandez and Fernando Herranz and Angel Llamazares and Miguel A. Sotelo and Luis M. Bergasa and Luis Magdalena”,
keywords = “Wireless localization”,
keywords = “WiFi signal strength sensor”,
keywords = “Fuzzy logic”,
keywords = “Fuzzy modeling ”
}

WiFi SLAM algorithms: an experimental comparison

Journal paper
F. Herranz, A. Llamazares, E. Molinos, M. Ocaña and M. A. Sotelo
Robotica, June 2014, ISSN: 1469-8668

Abstract

Localization and mapping in indoor environments, such as airports and hospitals, are key tasks for almost every robotic platform. Some researchers suggest the use of Range-Only (RO) sensors based on WiFi (Wireless Fidelity) technology with SLAM (Simultaneous Localization And Mapping) techniques to solve both problems. The current state of the art in RO SLAM is mainly focused on the filtering approach, while the study of smoothing approaches with RO sensors is quite incomplete. This paper presents a comparison between filtering algorithms, such as EKF and FastSLAM, and a smoothing algorithm, the SAM (Smoothing And Mapping). Experimental results are obtained in indoor environments using WiFi sensors. The results demonstrate the feasibility of the smoothing approach using WiFi sensors in an indoor environment.


BibTex:
@article{ROB:9305338,
author = {Herranz,F. and Llamazares,A. and Molinos,E. and Ocaña,M. and Sotelo,M. A.},
title = {WiFi SLAM algorithms: an experimental comparison},
journal = {Robotica},
volume = {FirstView},
month = {7},
year = {2014},
issn = {1469-8668},
pages = {1–22},
numpages = {22},
doi = {10.1017/S0263574714001908},
URL = {http://journals.cambridge.org/article_S0263574714001908},
}

Automatic Traffic Signs and Panels Inspection System Using Computer Vision

Journal paper
A. González, M. A. García-Garrido, D. F. Llorca, M. Gavilán, J. P. Fernández, P. F. Alcantarilla, I. Parra, F. Herranz, L. M. Bergasa, M. A. Sotelo, P. Revenga
IEEE Transactions on Intelligent Transportation Systems, Vol. 12, no. 2, 2011

Abstract

Computer vision techniques applied to systems used on road maintenance, which are related either to traffic signs or to the road itself, are playing a major role in many countries because of the higher investment on public works of this kind. These systems are able to collect a wide range of information automatically and quickly, with the aim of improving road safety. In this context, the correct visibility of traffic signs and panels is vital for the safety of drivers. This paper describes an approach to the VISUAL Inspection of Signs and panEls (“VISUALISE”), which is an automatic inspection system, mounted onboard a vehicle, which performs inspection tasks at conventional driving speeds. VISUALISE allows for an improvement in the awareness of the road signaling state, supporting planning and decision making on the administration’s and infrastructure operators’ side. A description of the main computer vision techniques and some experimental results obtained from thousands of kilometers are presented. Finally, the conclusions of the system are described.


BibTex:
@ARTICLE{5682406,
author={Gonzalez, A. and Garrido, M.A. and Llorca, D.F. and Gavilan, M. and Fernandez, J.P. and Alcantarilla, P.F. and Parra, I. and Herranz, F. and Bergasa, L.M. and Sotelo, M.A. and Revenga de Toro, P.},
journal={Intelligent Transportation Systems, IEEE Transactions on},
title={Automatic Traffic Signs and Panels Inspection System Using Computer Vision},
year={2011},
month={June},
volume={12},
number={2},
pages={485-499},
keywords={automatic optical inspection;computer vision;decision making;maintenance engineering;planning;road safety;road traffic;structural panels;VISUALISE;automatic inspection system;automatic traffic signs;computer vision techniques;conventional driving speeds;decision making;driver safety;inspection tasks;panels inspection system;planning;public works;road maintenance;road safety;road signaling state;visual inspection of signs and panels;Cameras;Image color analysis;Inspection;Lighting;Roads;Transforms;Vehicles;Computer vision;dynamic inspection;retroreflection;traffic signs detection;traffic signs recognition},
doi={10.1109/TITS.2010.2098029},
ISSN={1524-9050},}

A Comparison of SLAM Algorithms with Range Only Sensors

Conference paper
F. Herranz, Á Llamazares, E. Molinos, M Ocaña
Robotics and Automation (ICRA), 2014 IEEE International Conference on , vol., no., pp.4606,4611, May 31 2014-June 7 2014

Abstract

Localization and mapping in indoor environments, such as airports and hospitals, are key tasks for almost every robotic platform. Some researchers suggest the use of RO (Range Only) sensors based on WiFi (Wireless Fidelity) technology with SLAM (Simultaneous Localization And Mapping) techniques. The current state of the art in RO SLAM is mainly focused on the filtering approach, while the study of smoothing approach with RO sensors is quite incomplete. This paper presents a comparison between a filtering algorithm, the EKF, and a smoothing algorithm, the SAM (Smoothing And Mapping). Experimental results are obtained, first in an outdoor environment using two types of RO sensors and then in an indoor environment with WiFi sensors. The results demonstrate the feasibility of the smoothing approach with WiFi sensors in indoors.


 

BibTex:

@INPROCEEDINGS{6907532,
author={Herranz, F. and Llamazares, A. and Molinos, E. and Ocana, M.},
booktitle={Robotics and Automation (ICRA), 2014 IEEE International Conference on},
title={A comparison of SLAM algorithms with range only sensors},
year={2014},
month={May},
pages={4606-4611},
keywords={Kalman filters;SLAM (robots);distance measurement;indoor environment;smoothing methods;wireless LAN;EKF;RO SLAM;SAM;SLAM algorithms;WiFi sensors;WiFi technology;airports;filtering algorithm;filtering approach;hospitals;indoor environment;outdoor environment;range only sensors;robotic platform;simultaneous localization and mapping technique;smoothing algorithm;smoothing approach;wireless fidelity technology;IEEE 802.11 Standards;Indoor environments;Robot kinematics;Simultaneous localization and mapping;Smoothing methods},
doi={10.1109/ICRA.2014.6907532},}

Camera pose estimation using particle filters

Conference paper
F. Herranz, K. Muthukrishnan, K. Langendoen
Indoor Positioning and Indoor Navigation (IPIN), 2011 International Conference on , vol., no., pp.1,8, 21-23 Sept. 2011

Abstract

In this paper we propose a pose estimation algorithm based on Particle filtering which uses LED sightings gathered from wireless sensor nodes (WSN) to estimate the pose of the camera. The LEDs act as (visual) markers for our pose estimation algorithm. We also compare the performance of our pose estimation algorithm against two reference algorithms – (i) Extended Kalman filtering (EKF) and (ii) Discrete Linear Transform (DLT) based approaches. The performance of all the three algorithms are evaluated for different camera frame rates, varying level of measurement noise and for different marker distribution. Our results (small-scale experimental and room-level simulation studies) show that the particle filtering algorithm gives an accuracy of a few millimetres in position and a few degrees in orientation.


BibTex:

@INPROCEEDINGS{6071919,
author={Herranz, F. and Muthukrishnan, K. and Langendoen, K.},
booktitle={Indoor Positioning and Indoor Navigation (IPIN), 2011 International Conference on},
title={Camera pose estimation using particle filters},
year={2011},
month={Sept},
pages={1-8},
keywords={Kalman filters;light emitting diodes;object detection;particle filtering (numerical methods);LED sightings;camera frame rates;camera pose estimation;discrete linear transform;extended Kalman filtering;marker distribution;particle filters;wireless sensor nodes;Atmospheric measurements;Cameras;Estimation;Light emitting diodes;Particle measurements;Vectors;Wireless sensor networks},
doi={10.1109/IPIN.2011.6071919},}

Human Activity Recognition applying Computational Intelligence techniques for fusing information related to WiFi positioning and body posture

Conference paper
A. Alvarez-Alvarez, J. M. Alonso, G. Trivino, N. Hernández, F. Herranz, Á. Llamazares, M. Ocaña
Fuzzy Systems (FUZZ), 2010 IEEE International Conference on , vol., no., pp.1,8, 18-23 July 2010

Abstract

This work presents a general framework for people indoor activity recognition. Firstly, a Wireless Fidelity (WiFi) localization system implemented as a Fuzzy Rule-based Classifier (FRBC) is used to obtain an approximate position at the level of discrete zones (office, corridor, meeting room, etc). Secondly, a Fuzzy Finite State Machine (FFSM) is used for human body posture recognition (seated, standing upright or walking). Finally, another FFSM combines both WiFi localization and posture recognition to obtain a robust, reliable, and easily understandable activity recognition system (working in the desk room, crossing the corridor, having a meeting, etc). Each user carries with a personal digital agenda (PDA) or smart-phone equipped with a WiFi interface for localization task and accelerometers for posture recognition. Our approach does not require adding new hardware to the experimental environment. It relies on the WiFi access points (APs) widely available in most public and private buildings. We include a practical experimentation where good results were achieved.


BibTex:

@INPROCEEDINGS{5584187,
author={Alvarez-Alvarez, A. and Alonso, J.M. and Trivino, G. and Hernandez, N. and Herranz, F. and Llamazares, A. and Ocana, M.},
booktitle={Fuzzy Systems (FUZZ), 2010 IEEE International Conference on},
title={Human activity recognition applying computational intelligence techniques for fusing information related to WiFi positioning and body posture},
year={2010},
month={July},
pages={1-8},
keywords={fuzzy set theory;pose estimation;wireless LAN;WiFi access point;WiFi interface;WiFi localization;WiFi positioning;accelerometer;computational intelligence;fuzzy finite state machine;fuzzy rule-based classifier;human activity recognition;human body posture recognition;indoor activity recognition;personal digital agenda;smart phone;wireless fidelity localization system;Accelerometers;Buildings;Humans;IEEE 802.11 Standards;Input variables;Pragmatics;Sensors},
doi={10.1109/FUZZY.2010.5584187},
ISSN={1098-7584},}

Automatic Information Extraction of Traffic Panels based on Computer Vision

Conference paper
A. González, L.M. Bergasa, M. Gavilán, M.A. Sotelo, F. Herranz, C. Fernández
Intelligent Transportation Systems, 2009. ITSC '09. 12th International IEEE Conference on , vol., no., pp.1,6, 4-7 Oct. 2009

Abstract

Computer vision systems used on road maintenance, either related to signs or to the road itself, are playing a major role in many countries because of the higher investment on public works of this kind. These systems are able to collect a wide range of information automatically and quickly, with the aim of improving road safety. In this context, the suitability of the information contained on the road signs located above the road, typically known as traffic panels, is vital for a correct and safe use by the road user. This paper describes an approach to the first steps of a developing system which will be able to make an inventory and to check the reliability of the information contained on the traffic panels, and whose final aim is to take part on an automatic visual inspection system of signs and panels.


BibTex:

@INPROCEEDINGS{5309872,
author={Gonzalez, A. and Bergasa, L.M. and Gavilan, M. and Sotelo, M.A. and Herranz, F. and Fernandez, C.},
booktitle={Intelligent Transportation Systems, 2009. ITSC ’09. 12th International IEEE Conference on},
title={Automatic information extraction of traffic panels based on computer vision},
year={2009},
month={Oct},
pages={1-6},
keywords={Hough transforms;automatic optical inspection;computer vision;feature extraction;image segmentation;road safety;road traffic;traffic engineering computing;Hough transform;automatic information extraction;automatic visual inspection system;computer vision system;image segmentation;inventory system;public works investment;road maintenance;road safety;traffic panel;traffic sign;Cameras;Computer vision;Data mining;Inspection;Intelligent transportation systems;Roads;Vehicles;Video recording;Video sequences;Visualization;Hough transform;image segmentation;panel reorientation;traffic panels},
doi={10.1109/ITSC.2009.5309872},}

WiFi localization system based on fuzzy logic to deal with signal variations

Conference paper
N. Hernandez, F. Herranz, M. Ocaña, L.M. Bergasa, J.M. Alonso, L. Magdalena
Emerging Technologies & Factory Automation, 2009. ETFA 2009. IEEE Conference on , vol., no., pp.1,6, 22-25 Sept. 2009

Abstract

The goal of this paper is to study some of the most important WiFi signal variations, large and small scale variations and how they affect to WiFi localization systems. Moreover, the paper shows how to use soft computing techniques to deal with these uncertainties in WiFi localization systems. This work describes how to reduce uncertainty produced by small scale variations in indoor environments using fuzzy techniques. Some experimental results and conclusions are presented.


BibTex:
@INPROCEEDINGS{5347015,
author={Hernandez, N. and Herranz, F. and Ocana, M. and Bergasa, L.M. and Alonso, J.M. and Magdalena, L.},
booktitle={Emerging Technologies Factory Automation, 2009. ETFA 2009. IEEE Conference on},
title={WiFi localization system based on Fuzzy Logic to deal with signal variations},
year={2009},
month={Sept},
pages={1-6},
keywords={fuzzy logic;wireless LAN;WiFi localization system;WiFi signal variations;fuzzy logic;soft computing techniques;Cities and towns;Fuzzy logic;Global Positioning System;Hospitals;Indoor environments;Inventory control;Position measurement;Radio frequency;Surveillance;Uncertainty},
doi={10.1109/ETFA.2009.5347015},
ISSN={1946-0759},}

Dynamic obstacle avoidance based on curvature arcs

Conference paper
E. Molinos, Á. Llamazares, M. Ocaña, F. Herranz
System Integration (SII), 2014 IEEE/SICE International Symposium on , vol., no., pp.186,191, 13-15 Dec. 2014

Abstract

Traditionally, obstacle avoidance algorithms have been developed and applied successfully to mobile robots that work in controlled and static environments. But, when working in real scenarios the problem becomes complex since the scenario is dynamic and the algorithms must be enhanced in order to deal with moving objects. In this paper we propose a new method based on the well known Curvature Velocity Method (CVM) and a probabilistic 3D occupancy and velocity grid, developed by the authors, that can deal with these dynamic scenarios. The proposal is validated in real and simulated environments.


BibTex:

@INPROCEEDINGS{7028035,
author={Molinos, E. and Llamazares, A. and Ocana, M. and Herranz, F.},
booktitle={System Integration (SII), 2014 IEEE/SICE International Symposium on},
title={Dynamic obstacle avoidance based on curvature arcs},
year={2014},
month={Dec},
pages={186-191},
keywords={collision avoidance;mobile robots;CVM;DOMap;curvature arcs;curvature velocity method;dynamic obstacle avoidance;dynamic occupancy mapping;mobile robots;probabilistic 3D occupancy grid;probabilistic 3D velocity grid;Collision avoidance;Heuristic algorithms;Prediction algorithms;Robot kinematics;Robot sensing systems;Turning},
doi={10.1109/SII.2014.7028035},}

Cloud Robotics in FIWARE: A Proof of Concept

Conference paperJournal paper
F. Herranz, J. Jaime, I. González, Á. Hernandez
Hybrid Artificial Intelligent Systems, Lecture Notes in Computer Science Volume 9121, 2015, pp 580-591

Abstract

Novel Cloud infrastructures and their extensive set of resources have potential to help robotics to overcome its limitations. Traditionally, those limitations have been related with the number of sensors that are equipped in the robots and their computational power. The drawbacks of these limitations can be reduced by using the benefits of cloud architectures such as cloud computing, Internet of Things (IoT) sensing and cloud storage. FIWARE is an open platform which integrates cloud capabilities and Generics Enablers (GE) to interact with the cloud. This paper proposes the development of a Robotics GE and it presents the integration of the new GE into the FIWARE architecture. Two are the main goals behind this integration, first to bring all the benefits that FIWARE provides to robotics, and second to facilitate the development of robotics applications to non-expert robotics developers. Finally, a real example of the integration is shown by means of a parking meter application that combines context information, robotics, and cloud computing of vision algorithms.


BibTex:

@incollection{
year={2015},
isbn={978-3-319-19643-5},
booktitle={Hybrid Artificial Intelligent Systems},
volume={9121},
series={Lecture Notes in Computer Science},
editor={Onieva, Enrique and Santos, Igor and Osaba, Eneko and Quintián, Héctor and Corchado, Emilio},
doi={10.1007/978-3-319-19644-2_48},
title={Cloud Robotics in FIWARE: A Proof of Concept},
url={http://dx.doi.org/10.1007/978-3-319-19644-2_48},
publisher={Springer International Publishing},
keywords={Robotics; FIWARE; Cloud},
author={Herranz, F. and Jaime, J. and González, I. and Hernández, Á.},
pages={580-591},
language={English}
}