RTAB-Map - Revision history

Labm2414 at 19:47, 27 October 2024

2024-10-27T19:47:28Z

← Older revision		Revision as of 19:47, 27 October 2024
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	<center>{{#ev:youtube\|CuWESlLfWpQ}} {{#ev:youtube\|SQiFs1z7qSY}}</center>		<center>{{#ev:youtube\|CuWESlLfWpQ}} {{#ev:youtube\|SQiFs1z7qSY}}</center>
	<center>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}</center>		<center>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}</center>
	~~</english>~~

	<!-- <french>		<!-- <french>

Labm2414 at 19:46, 27 October 2024

2024-10-27T19:46:13Z

← Older revision		Revision as of 19:46, 27 October 2024
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	<big>		<big>
	~~<!-- <english>~~
	[[Image:RTAB-Map.png\|link=http://introlab.github.io/rtabmap\|RTAB-Map]] RTAB-Map : Real-Time Appearance-Based Mapping		[[Image:RTAB-Map.png\|link=http://introlab.github.io/rtabmap\|RTAB-Map]] RTAB-Map : Real-Time Appearance-Based Mapping
	~~</english> -->~~
	<!-- <french~~> --~~>		<!-- <french>
	[[Image:RTAB-Map.png\|link=http://introlab.github.io/rtabmap\|RTAB-Map]] RTAB-Map : Cartographie temps réel basée sur l'apparence de l'environnement		[[Image:RTAB-Map.png\|link=http://introlab.github.io/rtabmap\|RTAB-Map]] RTAB-Map : Cartographie temps réel basée sur l'apparence de l'environnement
	~~<!--~~ </french> -->		</french> -->
	</big>		</big>

	~~<!--~~
	~~<english>~~
	== Description ==		== Description ==
	'''This page is about the loop closure detection approach used by RTAB-Map. For RGB-D mapping, visit [http://introlab.github.io/rtabmap introlab.github.io/rtabmap]'''.		'''This page is about the loop closure detection approach used by RTAB-Map. For RGB-D mapping, visit [http://introlab.github.io/rtabmap introlab.github.io/rtabmap]'''.
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	Over time, the amount of time required to process new observations increases with the size of the internal map, which may affect real-time processing.		Over time, the amount of time required to process new observations increases with the size of the internal map, which may affect real-time processing.
	RTAB-Map is a novel real-time loop closure detection approach for large-scale and long-term SLAM. Our approach is based on efficient memory management to keep computation time for each new observation under a fixed time limit, thus respecting real-time limit for long-term operation. Results demonstrate the approach's adaptability and scalability using two custom data sets and ten standard data sets.		RTAB-Map is a novel real-time loop closure detection approach for large-scale and long-term SLAM. Our approach is based on efficient memory management to keep computation time for each new observation under a fixed time limit, thus respecting real-time limit for long-term operation. Results demonstrate the approach's adaptability and scalability using two custom data sets and ten standard data sets.
	~~</english>~~
	~~-->~~		<!-- <french>
	<!-- <french~~> --~~>
	== Description ==		== Description ==
	'''Cette page est à propos de l'approche de détection de fermeture de boucle utilisée dans RTAB-Map. Pour la cartographie RGB-D, visitez [http://introlab.github.io/rtabmap introlab.github.io/rtabmap]'''.		'''Cette page est à propos de l'approche de détection de fermeture de boucle utilisée dans RTAB-Map. Pour la cartographie RGB-D, visitez [http://introlab.github.io/rtabmap introlab.github.io/rtabmap]'''.

	La détection de fermeture de boucle est le processus impliqué en SLAM (localisation et cartographie simultanées) lorsqu'on tente de trouver une correspondance entre un endroit présent et un autre déjà visité. Plus la carte interne augmente en taille, plus le temps requis pour la détection de fermeture de boucle augmente, ce qui peut affecter le traitement en temps réel. RTAB-Map est une nouvelle approche de détection de fermeture de boucle fonctionnant en temps réel pour du SLAM à grande échelle et à long terme. Notre approche est basée sur une gestion efficace de la mémoire afin de garder le temps de calcul en dessous d'un seuil de temps, respectant ainsi la limite de temps réel à long terme. En utilisant dix ensembles de données standards, notre propre ensemble de données dérivées d'un parcours de plus de 2 km rassemblant des conditions diverses et notre ensemble de données montrant un parcours où le robot visite les mêmes endroits une centaine de fois, les résultats démontrent l'adaptabilité et l'extensibilité de notre approche.		La détection de fermeture de boucle est le processus impliqué en SLAM (localisation et cartographie simultanées) lorsqu'on tente de trouver une correspondance entre un endroit présent et un autre déjà visité. Plus la carte interne augmente en taille, plus le temps requis pour la détection de fermeture de boucle augmente, ce qui peut affecter le traitement en temps réel. RTAB-Map est une nouvelle approche de détection de fermeture de boucle fonctionnant en temps réel pour du SLAM à grande échelle et à long terme. Notre approche est basée sur une gestion efficace de la mémoire afin de garder le temps de calcul en dessous d'un seuil de temps, respectant ainsi la limite de temps réel à long terme. En utilisant dix ensembles de données standards, notre propre ensemble de données dérivées d'un parcours de plus de 2 km rassemblant des conditions diverses et notre ensemble de données montrant un parcours où le robot visite les mêmes endroits une centaine de fois, les résultats démontrent l'adaptabilité et l'extensibilité de notre approche.
	~~<!--~~ </french> -->		</french> -->
	<center>		<center>
	{{#ev:youtube\|71eRxTc1DaU}}		{{#ev:youtube\|71eRxTc1DaU}}
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	</center>		</center>

	~~<english>~~
	== Results ==		== Results ==
	''Note that these results (more recent) may differ from those in the presentation video above...''		''Note that these results (more recent) may differ from those in the presentation video above...''
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	</english>		</english>

	<french>		<!-- <french>
	== Résultats ==		== Résultats ==
	''À noter que les résultats (plus récents) présentés ici peuvent différer de ceux dans le vidéo...''		''À noter que les résultats (plus récents) présentés ici peuvent différer de ceux dans le vidéo...''
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	<center>{{#ev:youtube\|CuWESlLfWpQ}} {{#ev:youtube\|SQiFs1z7qSY}}</center>		<center>{{#ev:youtube\|CuWESlLfWpQ}} {{#ev:youtube\|SQiFs1z7qSY}}</center>
	<center>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}</center>		<center>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}</center>
	</french>		</french> -->


	~~<english>~~
	== Source code ==		== Source code ==
	The code was tested on Windows (Xp, 7), Mac OS X 10.6 and Ubuntu 10.4LTS.		The code was tested on Windows (Xp, 7), Mac OS X 10.6 and Ubuntu 10.4LTS.
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	[[File:RTAB-Map_Interface.png\|800px\|Images acquired in Need For Speed Most Wanted]]		[[File:RTAB-Map_Interface.png\|800px\|Images acquired in Need For Speed Most Wanted]]
	</div>		</div>
	<~~/english>~~
	<french>		<!-- <french>
	== Code source ==		== Code source ==
	Le code a été testé sur Windows (Xp, 7), Mac OS X 10.6 et Ubuntu 10.4LTS.		Le code a été testé sur Windows (Xp, 7), Mac OS X 10.6 et Ubuntu 10.4LTS.
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	[[File:RTAB-Map_Interface.png\|800px\|Images provenant de Need For Speed Most Wanted]]		[[File:RTAB-Map_Interface.png\|800px\|Images provenant de Need For Speed Most Wanted]]
	</div>		</div>
	</french>		</french> -->
	~~<english>~~
	== Data sets ==		== Data sets ==
	'''UdeS'''		'''UdeS'''
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	* [[Media:malaga2009_campus_2L.png\|malaga2009_campus_2L.png]] 653 images at ~1 Hz		* [[Media:malaga2009_campus_2L.png\|malaga2009_campus_2L.png]] 653 images at ~1 Hz
	* [[Media:malaga2009_parking_6L.png\|malaga2009_parking_6L.png]] 435 images at ~1 Hz		* [[Media:malaga2009_parking_6L.png\|malaga2009_parking_6L.png]] 435 images at ~1 Hz
	<~~/english>~~
	<french>		<!-- <french>
	== Ensembles de données ==		== Ensembles de données ==
	'''UdeS'''		'''UdeS'''
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	* [[Media:malaga2009_campus_2L.png\|malaga2009_campus_2L.png]] 653 images à ~1 Hz		* [[Media:malaga2009_campus_2L.png\|malaga2009_campus_2L.png]] 653 images à ~1 Hz
	* [[Media:malaga2009_parking_6L.png\|malaga2009_parking_6L.png]] 435 images à ~1 Hz		* [[Media:malaga2009_parking_6L.png\|malaga2009_parking_6L.png]] 435 images à ~1 Hz
	</french>		</french> -->

	== Publications ==		== Publications ==
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	* M. Labbé, "Simultaneous Localization and Mapping (SLAM) with RTAB-Map", Université Laval, Québec, November 2015 ([[Media:Labbe2015ULaval.pdf\|slides pdf]])		* M. Labbé, "Simultaneous Localization and Mapping (SLAM) with RTAB-Map", Université Laval, Québec, November 2015 ([[Media:Labbe2015ULaval.pdf\|slides pdf]])

	~~<english>~~
	== Team ==		== Team ==
	* [[Mathieu Labbé]]		* [[Mathieu Labbé]]
	* [http://www.gel.usherbrooke.ca/michaudf/ François Michaud]		* [http://www.gel.usherbrooke.ca/michaudf/ François Michaud]
	<~~/english>~~
	<french>		<!-- <french>
	== Équipe ==		== Équipe ==
	* [[Mathieu Labbé]]		* [[Mathieu Labbé]]
	* [http://www.gel.usherbrooke.ca/michaudf/ François Michaud]		* [http://www.gel.usherbrooke.ca/michaudf/ François Michaud]
	</french>		</french> -->

Letd2801 at 12:48, 28 May 2024

2024-05-28T12:48:57Z

← Older revision		Revision as of 12:48, 28 May 2024
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	</big>		</big>

			<!--
	<english>		<english>
	== Description ==		== Description ==
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	Over time, the amount of time required to process new observations increases with the size of the internal map, which may affect real-time processing.		Over time, the amount of time required to process new observations increases with the size of the internal map, which may affect real-time processing.
	RTAB-Map is a novel real-time loop closure detection approach for large-scale and long-term SLAM. Our approach is based on efficient memory management to keep computation time for each new observation under a fixed time limit, thus respecting real-time limit for long-term operation. Results demonstrate the approach's adaptability and scalability using two custom data sets and ten standard data sets.		RTAB-Map is a novel real-time loop closure detection approach for large-scale and long-term SLAM. Our approach is based on efficient memory management to keep computation time for each new observation under a fixed time limit, thus respecting real-time limit for long-term operation. Results demonstrate the approach's adaptability and scalability using two custom data sets and ten standard data sets.
	</english><french>		</english>
			-->
			<!-- <french> -->
	== Description ==		== Description ==
	'''Cette page est à propos de l'approche de détection de fermeture de boucle utilisée dans RTAB-Map. Pour la cartographie RGB-D, visitez [http://introlab.github.io/rtabmap introlab.github.io/rtabmap]'''.		'''Cette page est à propos de l'approche de détection de fermeture de boucle utilisée dans RTAB-Map. Pour la cartographie RGB-D, visitez [http://introlab.github.io/rtabmap introlab.github.io/rtabmap]'''.

	La détection de fermeture de boucle est le processus impliqué en SLAM (localisation et cartographie simultanées) lorsqu'on tente de trouver une correspondance entre un endroit présent et un autre déjà visité. Plus la carte interne augmente en taille, plus le temps requis pour la détection de fermeture de boucle augmente, ce qui peut affecter le traitement en temps réel. RTAB-Map est une nouvelle approche de détection de fermeture de boucle fonctionnant en temps réel pour du SLAM à grande échelle et à long terme. Notre approche est basée sur une gestion efficace de la mémoire afin de garder le temps de calcul en dessous d'un seuil de temps, respectant ainsi la limite de temps réel à long terme. En utilisant dix ensembles de données standards, notre propre ensemble de données dérivées d'un parcours de plus de 2 km rassemblant des conditions diverses et notre ensemble de données montrant un parcours où le robot visite les mêmes endroits une centaine de fois, les résultats démontrent l'adaptabilité et l'extensibilité de notre approche.		La détection de fermeture de boucle est le processus impliqué en SLAM (localisation et cartographie simultanées) lorsqu'on tente de trouver une correspondance entre un endroit présent et un autre déjà visité. Plus la carte interne augmente en taille, plus le temps requis pour la détection de fermeture de boucle augmente, ce qui peut affecter le traitement en temps réel. RTAB-Map est une nouvelle approche de détection de fermeture de boucle fonctionnant en temps réel pour du SLAM à grande échelle et à long terme. Notre approche est basée sur une gestion efficace de la mémoire afin de garder le temps de calcul en dessous d'un seuil de temps, respectant ainsi la limite de temps réel à long terme. En utilisant dix ensembles de données standards, notre propre ensemble de données dérivées d'un parcours de plus de 2 km rassemblant des conditions diverses et notre ensemble de données montrant un parcours où le robot visite les mêmes endroits une centaine de fois, les résultats démontrent l'adaptabilité et l'extensibilité de notre approche.
	</french>		<!-- </french> -->
	<center>		<center>
	{{#ev:youtube\|71eRxTc1DaU}}		{{#ev:youtube\|71eRxTc1DaU}}
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	<center>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}</center>		<center>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}</center>
	</english>		</english>

	<french>		<french>
	== Résultats ==		== Résultats ==

Letd2801 at 12:45, 28 May 2024

2024-05-28T12:45:39Z

← Older revision		Revision as of 12:45, 28 May 2024
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	<~~analytics uacct="UA-27707792-1" ></analytics~~>
	<~~big>~~<english>[[Image:RTAB-Map.png\|link=http://introlab.github.io/rtabmap\|RTAB-Map]] RTAB-Map : Real-Time Appearance-Based Mapping</english><french>[[Image:RTAB-Map.png\|link=http://introlab.github.io/rtabmap\|RTAB-Map]] RTAB-Map : Cartographie temps réel basée sur l'apparence de l'environnement </french></big>		<big>
			<!-- <english>
			[[Image:RTAB-Map.png\|link=http://introlab.github.io/rtabmap\|RTAB-Map]] RTAB-Map : Real-Time Appearance-Based Mapping
			</english> -->
			<!-- <french> -->
			[[Image:RTAB-Map.png\|link=http://introlab.github.io/rtabmap\|RTAB-Map]] RTAB-Map : Cartographie temps réel basée sur l'apparence de l'environnement
			<!-- </french> -->
			</big>

	<english>		<english>

Labm2414 at 04:08, 27 June 2022

2022-06-27T04:08:17Z

← Older revision		Revision as of 04:08, 27 June 2022
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	== Publications ==		== Publications ==
			# M. Labbé and F. Michaud, “Multi-Session Visual SLAM for Illumination-Invariant Re-Localization in Indoor Environments,” in ''Frontiers in Robotics and AI'', vol. 9, 2022. ([https://arxiv.org/abs/2103.03827 pdf]) ([https://doi.org/10.3389/frobt.2022.801886 Frontiers])
	# M. Labbé and F. Michaud, “RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation,” in ''Journal of Field Robotics'', vol. 36, no. 2, pp. 416–446, 2019. ([[Media:Labbe18JFR_preprint.pdf\|pdf]]) ([https://doi.org/10.1002/rob.21831 Wiley])		# M. Labbé and F. Michaud, “RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation,” in ''Journal of Field Robotics'', vol. 36, no. 2, pp. 416–446, 2019. ([[Media:Labbe18JFR_preprint.pdf\|pdf]]) ([https://doi.org/10.1002/rob.21831 Wiley])
	# M. Labbé and F. Michaud, “Long-term online multi-session graph-based SPLAM with memory management,” in ''Autonomous Robots'', vol. 42, no. 6, pp. 1133-1150, 2017. ([[Media:LabbeAURO2017.pdf\|pdf]]) ([http://dx.doi.org/10.1007/s10514-017-9682-5 Springer])		# M. Labbé and F. Michaud, “Long-term online multi-session graph-based SPLAM with memory management,” in ''Autonomous Robots'', vol. 42, no. 6, pp. 1133-1150, 2017. ([[Media:LabbeAURO2017.pdf\|pdf]]) ([http://dx.doi.org/10.1007/s10514-017-9682-5 Springer])

Labm2414 at 22:28, 17 April 2019

2019-04-17T22:28:59Z

← Older revision		Revision as of 22:28, 17 April 2019
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	== Publications ==		== Publications ==
	# M. Labbé and F. Michaud, “RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation,” in ''Journal of Field Robotics'', ~~accepted~~, ~~2018~~. ([[Media:Labbe18JFR_preprint.pdf\|pdf]]) ([https://doi.org/10.1002/rob.21831 Wiley])		# M. Labbé and F. Michaud, “RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation,” in ''Journal of Field Robotics'', vol. 36, no. 2, pp. 416–446, 2019. ([[Media:Labbe18JFR_preprint.pdf\|pdf]]) ([https://doi.org/10.1002/rob.21831 Wiley])
	# M. Labbé and F. Michaud, “Long-term online multi-session graph-based SPLAM with memory management,” in ''Autonomous Robots'', vol. 42, no. 6, pp. 1133-1150, 2017. ([[Media:LabbeAURO2017.pdf\|pdf]]) ([http://dx.doi.org/10.1007/s10514-017-9682-5 Springer])		# M. Labbé and F. Michaud, “Long-term online multi-session graph-based SPLAM with memory management,” in ''Autonomous Robots'', vol. 42, no. 6, pp. 1133-1150, 2017. ([[Media:LabbeAURO2017.pdf\|pdf]]) ([http://dx.doi.org/10.1007/s10514-017-9682-5 Springer])
	#M. Labbé and F. Michaud, “Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM,” in ''Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems'', 2014. ([[Media:Labbe14-IROS.pdf\|pdf]]) ([http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6942926 IEEE Xplore])		#M. Labbé and F. Michaud, “Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM,” in ''Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems'', 2014. ([[Media:Labbe14-IROS.pdf\|pdf]]) ([http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6942926 IEEE Xplore])

Labm2414 at 18:12, 28 December 2018

2018-12-28T18:12:35Z

← Older revision		Revision as of 18:12, 28 December 2018
Line 270:		Line 270:

	== Publications ==		== Publications ==
	#M. Labbé and F. Michaud, “Long-term online multi-session graph-based SPLAM with memory management,” in ''Autonomous Robots'', ~~accepted~~, 2017. ([[Media:LabbeAURO2017.pdf\|pdf]]) ([http://dx.doi.org/10.1007/s10514-017-9682-5 Springer])		# M. Labbé and F. Michaud, “RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation,” in ''Journal of Field Robotics'', accepted, 2018. ([[Media:Labbe18JFR_preprint.pdf\|pdf]]) ([https://doi.org/10.1002/rob.21831 Wiley])
			# M. Labbé and F. Michaud, “Long-term online multi-session graph-based SPLAM with memory management,” in ''Autonomous Robots'', vol. 42, no. 6, pp. 1133-1150, 2017. ([[Media:LabbeAURO2017.pdf\|pdf]]) ([http://dx.doi.org/10.1007/s10514-017-9682-5 Springer])
	#M. Labbé and F. Michaud, “Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM,” in ''Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems'', 2014. ([[Media:Labbe14-IROS.pdf\|pdf]]) ([http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6942926 IEEE Xplore])		#M. Labbé and F. Michaud, “Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM,” in ''Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems'', 2014. ([[Media:Labbe14-IROS.pdf\|pdf]]) ([http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6942926 IEEE Xplore])
	#Labbé, M., Michaud., F. (2013), “Appearance-based loop closure detection in real-time for large-scale and long-term operation,” ''IEEE Transactions on Robotics'', vol. 29, no. 3, pp. 734-745. ([[Media:TRO2013.pdf\|pdf]]) ([http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6459608 IEEE Xplore])		#Labbé, M., Michaud., F. (2013), “Appearance-based loop closure detection in real-time for large-scale and long-term operation,” ''IEEE Transactions on Robotics'', vol. 29, no. 3, pp. 734-745. ([[Media:TRO2013.pdf\|pdf]]) ([http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6459608 IEEE Xplore])

Letd2801 at 19:12, 28 November 2018

2018-11-28T19:12:27Z

← Older revision		Revision as of 19:12, 28 November 2018
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	</french>		</french>
	<center>		<center>
	~~<youtube>https://www.youtube.com/watch?v=71eRxTc1DaU</youtube>~~
	~~<youtube>https://www.youtube.com/watch?v=CAk-QGMlQmI</youtube>~~
	~~<youtube>https://www.youtube.com/watch?v=AMLwjo80WzI</youtube>~~
	{{#ev:youtube\|71eRxTc1DaU}}		{{#ev:youtube\|71eRxTc1DaU}}
	{{#ev:youtube\|CAk-QGMlQmI}}		{{#ev:youtube\|CAk-QGMlQmI}}
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	'''Videos'''		'''Videos'''
	* Newer:		* Newer:
	<center~~><code~~>{{#ev:youtube\|1dImRinTJSE}}</code></center>		<center>{{#ev:youtube\|1dImRinTJSE}}</code></center>
	<center~~><code~~>{{#ev:youtube\|N5q0jQrV3gw}} {{#ev:youtube\|PqO_x8tcFiY}}~~</code>~~</center>		<center>{{#ev:youtube\|N5q0jQrV3gw}} {{#ev:youtube\|PqO_x8tcFiY}}</center>
	<center~~><code~~>{{#ev:youtube\|2MogQIT_B2I}} {{#ev:youtube\|AH_oKp3CrRA}}~~</code>~~</center>		<center>{{#ev:youtube\|2MogQIT_B2I}} {{#ev:youtube\|AH_oKp3CrRA}}</center>
	<center~~><code~~>{{#ev:youtube\|0fNUD11FNZU}} {{#ev:youtube\|ViXlUywWHYQ}}~~</code>~~</center>		<center>{{#ev:youtube\|0fNUD11FNZU}} {{#ev:youtube\|ViXlUywWHYQ}}</center>
	* Older:		* Older:
	<center~~><code~~>{{#ev:youtube\|0zWs6jTaAwQ}} {{#ev:youtube\|J8KGEA9ecS0}}~~</code>~~</center>		<center>{{#ev:youtube\|0zWs6jTaAwQ}} {{#ev:youtube\|J8KGEA9ecS0}}</center>
	<center~~><code~~>{{#ev:youtube\|kghs6XM8Yzw}} {{#ev:youtube\|awV2Xbjq7OM}}~~</code>~~</center>		<center>{{#ev:youtube\|kghs6XM8Yzw}} {{#ev:youtube\|awV2Xbjq7OM}}</center>
	<center~~><code~~>{{#ev:youtube\|CuWESlLfWpQ}} {{#ev:youtube\|SQiFs1z7qSY}}~~</code>~~</center>		<center>{{#ev:youtube\|CuWESlLfWpQ}} {{#ev:youtube\|SQiFs1z7qSY}}</center>
	<center~~><code~~>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}~~</code>~~</center>		<center>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}</center>
	</english>		</english>
	<french>		<french>
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	'''Vidéos'''		'''Vidéos'''
	* Nouveaux:		* Nouveaux:
	<center~~><code~~>{{#ev:youtube\|1dImRinTJSE}}~~</code>~~</center>		<center>{{#ev:youtube\|1dImRinTJSE}}</center>
	<center~~><code~~>{{#ev:youtube\|N5q0jQrV3gw}} {{#ev:youtube\|PqO_x8tcFiY}}~~</code>~~</center>		<center>{{#ev:youtube\|N5q0jQrV3gw}} {{#ev:youtube\|PqO_x8tcFiY}}</center>
	<center~~><code~~>{{#ev:youtube\|2MogQIT_B2I}} {{#ev:youtube\|AH_oKp3CrRA}}~~</code>~~</center>		<center>{{#ev:youtube\|2MogQIT_B2I}} {{#ev:youtube\|AH_oKp3CrRA}}</center>
	<center~~><code~~>{{#ev:youtube\|0fNUD11FNZU}} {{#ev:youtube\|ViXlUywWHYQ}}~~</code>~~</center>		<center>{{#ev:youtube\|0fNUD11FNZU}} {{#ev:youtube\|ViXlUywWHYQ}}</center>
	* Anciens:		* Anciens:
	<center~~><code~~>{{#ev:youtube\|0zWs6jTaAwQ}} {{#ev:youtube\|J8KGEA9ecS0}}~~</code>~~</center>		<center>{{#ev:youtube\|0zWs6jTaAwQ}} {{#ev:youtube\|J8KGEA9ecS0}}</center>
	<center~~><code~~>{{#ev:youtube\|kghs6XM8Yzw}} {{#ev:youtube\|awV2Xbjq7OM}}~~</code>~~</center>		<center>{{#ev:youtube\|kghs6XM8Yzw}} {{#ev:youtube\|awV2Xbjq7OM}}</center>
	<center~~><code~~>{{#ev:youtube\|CuWESlLfWpQ}} {{#ev:youtube\|SQiFs1z7qSY}}~~</code>~~</center>		<center>{{#ev:youtube\|CuWESlLfWpQ}} {{#ev:youtube\|SQiFs1z7qSY}}</center>
	<center~~><code~~>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}~~</code>~~</center>		<center>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}</center>
	</french>		</french>

Letd2801 at 19:10, 28 November 2018

2018-11-28T19:10:28Z

← Older revision		Revision as of 19:10, 28 November 2018
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	<youtube>https://www.youtube.com/watch?v=CAk-QGMlQmI</youtube>		<youtube>https://www.youtube.com/watch?v=CAk-QGMlQmI</youtube>
	<youtube>https://www.youtube.com/watch?v=AMLwjo80WzI</youtube>		<youtube>https://www.youtube.com/watch?v=AMLwjo80WzI</youtube>
	~~<code>~~{{#ev:youtube\|71eRxTc1DaU}}~~</code>~~		{{#ev:youtube\|71eRxTc1DaU}}
	~~<code>~~{{#ev:youtube\|CAk-QGMlQmI}}~~</code>~~		{{#ev:youtube\|CAk-QGMlQmI}}
	~~<code>~~{{#ev:youtube\|AMLwjo80WzI}}~~</code>~~		{{#ev:youtube\|AMLwjo80WzI}}
	</center>		</center>

Letd2801 at 19:08, 28 November 2018

2018-11-28T19:08:50Z

← Older revision		Revision as of 19:08, 28 November 2018
Line 16:		Line 16:
	</french>		</french>
	<center>		<center>
			<youtube>https://www.youtube.com/watch?v=71eRxTc1DaU</youtube>
			<youtube>https://www.youtube.com/watch?v=CAk-QGMlQmI</youtube>
			<youtube>https://www.youtube.com/watch?v=AMLwjo80WzI</youtube>
	<code>{{#ev:youtube\|71eRxTc1DaU}}</code>		<code>{{#ev:youtube\|71eRxTc1DaU}}</code>
	<code>{{#ev:youtube\|CAk-QGMlQmI}}</code>		<code>{{#ev:youtube\|CAk-QGMlQmI}}</code>

← Older revision		Revision as of 18:12, 28 December 2018
Line 270:		Line 270:

	== Publications ==		== Publications ==
	#M. Labbé and F. Michaud, “Long-term online multi-session graph-based SPLAM with memory management,” in ''Autonomous Robots'', ~~accepted~~, 2017. ([[Media:LabbeAURO2017.pdf\|pdf]]) ([http://dx.doi.org/10.1007/s10514-017-9682-5 Springer])		# M. Labbé and F. Michaud, “RTAB-Map as an Open-Source Lidar and Visual SLAM Library for Large-Scale and Long-Term Online Operation,” in ''Journal of Field Robotics'', accepted, 2018. ([[Media:Labbe18JFR_preprint.pdf\|pdf]]) ([https://doi.org/10.1002/rob.21831 Wiley])
			# M. Labbé and F. Michaud, “Long-term online multi-session graph-based SPLAM with memory management,” in ''Autonomous Robots'', vol. 42, no. 6, pp. 1133-1150, 2017. ([[Media:LabbeAURO2017.pdf\|pdf]]) ([http://dx.doi.org/10.1007/s10514-017-9682-5 Springer])
	#M. Labbé and F. Michaud, “Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM,” in ''Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems'', 2014. ([[Media:Labbe14-IROS.pdf\|pdf]]) ([http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6942926 IEEE Xplore])		#M. Labbé and F. Michaud, “Online Global Loop Closure Detection for Large-Scale Multi-Session Graph-Based SLAM,” in ''Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems'', 2014. ([[Media:Labbe14-IROS.pdf\|pdf]]) ([http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6942926 IEEE Xplore])
	#Labbé, M., Michaud., F. (2013), “Appearance-based loop closure detection in real-time for large-scale and long-term operation,” ''IEEE Transactions on Robotics'', vol. 29, no. 3, pp. 734-745. ([[Media:TRO2013.pdf\|pdf]]) ([http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6459608 IEEE Xplore])		#Labbé, M., Michaud., F. (2013), “Appearance-based loop closure detection in real-time for large-scale and long-term operation,” ''IEEE Transactions on Robotics'', vol. 29, no. 3, pp. 734-745. ([[Media:TRO2013.pdf\|pdf]]) ([http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6459608 IEEE Xplore])

← Older revision		Revision as of 19:12, 28 November 2018
Line 16:		Line 16:
	</french>		</french>
	<center>		<center>
	~~<youtube>https://www.youtube.com/watch?v=71eRxTc1DaU</youtube>~~
	~~<youtube>https://www.youtube.com/watch?v=CAk-QGMlQmI</youtube>~~
	~~<youtube>https://www.youtube.com/watch?v=AMLwjo80WzI</youtube>~~
	{{#ev:youtube\|71eRxTc1DaU}}		{{#ev:youtube\|71eRxTc1DaU}}
	{{#ev:youtube\|CAk-QGMlQmI}}		{{#ev:youtube\|CAk-QGMlQmI}}
Line 49:		Line 46:
	'''Videos'''		'''Videos'''
	* Newer:		* Newer:
	<center~~><code~~>{{#ev:youtube\|1dImRinTJSE}}</code></center>		<center>{{#ev:youtube\|1dImRinTJSE}}</code></center>
	<center~~><code~~>{{#ev:youtube\|N5q0jQrV3gw}} {{#ev:youtube\|PqO_x8tcFiY}}~~</code>~~</center>		<center>{{#ev:youtube\|N5q0jQrV3gw}} {{#ev:youtube\|PqO_x8tcFiY}}</center>
	<center~~><code~~>{{#ev:youtube\|2MogQIT_B2I}} {{#ev:youtube\|AH_oKp3CrRA}}~~</code>~~</center>		<center>{{#ev:youtube\|2MogQIT_B2I}} {{#ev:youtube\|AH_oKp3CrRA}}</center>
	<center~~><code~~>{{#ev:youtube\|0fNUD11FNZU}} {{#ev:youtube\|ViXlUywWHYQ}}~~</code>~~</center>		<center>{{#ev:youtube\|0fNUD11FNZU}} {{#ev:youtube\|ViXlUywWHYQ}}</center>
	* Older:		* Older:
	<center~~><code~~>{{#ev:youtube\|0zWs6jTaAwQ}} {{#ev:youtube\|J8KGEA9ecS0}}~~</code>~~</center>		<center>{{#ev:youtube\|0zWs6jTaAwQ}} {{#ev:youtube\|J8KGEA9ecS0}}</center>
	<center~~><code~~>{{#ev:youtube\|kghs6XM8Yzw}} {{#ev:youtube\|awV2Xbjq7OM}}~~</code>~~</center>		<center>{{#ev:youtube\|kghs6XM8Yzw}} {{#ev:youtube\|awV2Xbjq7OM}}</center>
	<center~~><code~~>{{#ev:youtube\|CuWESlLfWpQ}} {{#ev:youtube\|SQiFs1z7qSY}}~~</code>~~</center>		<center>{{#ev:youtube\|CuWESlLfWpQ}} {{#ev:youtube\|SQiFs1z7qSY}}</center>
	<center~~><code~~>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}~~</code>~~</center>		<center>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}</center>
	</english>		</english>
	<french>		<french>
Line 84:		Line 81:
	'''Vidéos'''		'''Vidéos'''
	* Nouveaux:		* Nouveaux:
	<center~~><code~~>{{#ev:youtube\|1dImRinTJSE}}~~</code>~~</center>		<center>{{#ev:youtube\|1dImRinTJSE}}</center>
	<center~~><code~~>{{#ev:youtube\|N5q0jQrV3gw}} {{#ev:youtube\|PqO_x8tcFiY}}~~</code>~~</center>		<center>{{#ev:youtube\|N5q0jQrV3gw}} {{#ev:youtube\|PqO_x8tcFiY}}</center>
	<center~~><code~~>{{#ev:youtube\|2MogQIT_B2I}} {{#ev:youtube\|AH_oKp3CrRA}}~~</code>~~</center>		<center>{{#ev:youtube\|2MogQIT_B2I}} {{#ev:youtube\|AH_oKp3CrRA}}</center>
	<center~~><code~~>{{#ev:youtube\|0fNUD11FNZU}} {{#ev:youtube\|ViXlUywWHYQ}}~~</code>~~</center>		<center>{{#ev:youtube\|0fNUD11FNZU}} {{#ev:youtube\|ViXlUywWHYQ}}</center>
	* Anciens:		* Anciens:
	<center~~><code~~>{{#ev:youtube\|0zWs6jTaAwQ}} {{#ev:youtube\|J8KGEA9ecS0}}~~</code>~~</center>		<center>{{#ev:youtube\|0zWs6jTaAwQ}} {{#ev:youtube\|J8KGEA9ecS0}}</center>
	<center~~><code~~>{{#ev:youtube\|kghs6XM8Yzw}} {{#ev:youtube\|awV2Xbjq7OM}}~~</code>~~</center>		<center>{{#ev:youtube\|kghs6XM8Yzw}} {{#ev:youtube\|awV2Xbjq7OM}}</center>
	<center~~><code~~>{{#ev:youtube\|CuWESlLfWpQ}} {{#ev:youtube\|SQiFs1z7qSY}}~~</code>~~</center>		<center>{{#ev:youtube\|CuWESlLfWpQ}} {{#ev:youtube\|SQiFs1z7qSY}}</center>
	<center~~><code~~>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}~~</code>~~</center>		<center>{{#ev:youtube\|ShQlakkzsY4}} {{#ev:youtube\|cTmf5yrpcl8}}</center>
	</french>		</french>