nach oben

Autonomous Robots

Erschienen in:

26.07.2017

Are you ABLE to perform a life-long visual topological localization?

verfasst von: Roberto Arroyo, Pablo F. Alcantarilla, Luis M. Bergasa, Eduardo Romera

Erschienen in: Autonomous Robots | Ausgabe 3/2018

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Visual topological localization is a process typically required by varied mobile autonomous robots, but it is a complex task if long operating periods are considered. This is because of the appearance variations suffered in a place: dynamic elements, illumination or weather. Due to these problems, long-term visual place recognition across seasons has become a challenge for the robotics community. For this reason, we propose an innovative method for a robust and efficient life-long localization using cameras. In this paper, we describe our approach (ABLE), which includes three different versions depending on the type of images: monocular, stereo and panoramic. This distinction makes our proposal more adaptable and effective, because it allows to exploit the extra information that can be provided by each type of camera. Besides, we contribute a novel methodology for identifying places, which is based on a fast matching of global binary descriptors extracted from sequences of images. The presented results demonstrate the benefits of using ABLE, which is compared to the most representative state-of-the-art algorithms in long-term conditions.

Vorheriger Artikel Range-only SLAM for robot-sensor network cooperation

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

OpenCV is available from: http://opencv.org/.

More information, extra material, videos and open code (2016b Arroyo, Alcantarilla, Bergasa, and Romera) about ABLE are available from the website of the project: http://www.robesafe.com/personal/roberto.arroyo/openable.html.

Alahi, A., Ortiz, R., & Vandergheynst, P. (2012). FREAK: Fast retina keypoint. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Vol. 2, pp. 510–517). doi:10.1109/CVPR.2012.6247715.

Alcantarilla, P. F., Stasse, O., Druon, S., Bergasa, L. M., & Dellaert, F. (2013). How to localize humanoids with a single camera? Autonomous Robots, 34(1), 47–71. doi:10.1007/s10514-012-9312-1.CrossRef

Alcantarilla, P. F., Stent, S., Ros, G., Arroyo, R., & Gherardi, R. (2016). Street-view change detection with deconvolutional networks. In Robotics Science and Systems Conference (RSS) (pp. 1–10). doi:10.15607/RSS.2016.XII.044.

Arroyo, R., Alcantarilla, P. F., Bergasa, L. M., Yebes, J. J., & Bronte, S. (2014a). Fast and effective visual place recognition using binary codes and disparity information. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3089–3094). doi:10.1109/IROS.2014.6942989.

Arroyo, R., Alcantarilla, P. F., Bergasa, L. M., Yebes, J. J., & Gámez, S. (2014b). Bidirectional loop closure detection on panoramas for visual navigation. In IEEE Intelligent Vehicles Symposium (IV) (pp. 1378–1383). doi:10.1109/IVS.2014.6856457.

Arroyo, R., Alcantarilla, P. F., Bergasa, L. M., & Romera, E. (2015). Towards life-long visual localization using an efficient matching of binary sequences from images. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 6328–6335). doi:10.1109/ICRA.2015.7140088.

Arroyo, R., Alcantarilla, P. F., Bergasa, L. M., & Romera, E. (2016a). Fusion and binarization of CNN features for robust topological localization across seasons. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 4656–4663). doi:10.1109/IROS.2016.7759685.

Arroyo, R., Alcantarilla, P. F., Bergasa, L. M., & Romera, E. (2016b). OpenABLE: An open-source toolbox for application in life-long visual localization of autonomous vehicles. In IEEE Intelligent Transportation Systems Conference (ITSC) (pp. 965–970). doi:10.1109/ITSC.2016.7795672.

Badino, H., Huber, D. F., & Kanade, T. (2012). Real-time topometric localization. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 1635–1642). doi:10.1109/ICRA.2012.6224716.

Bailey, T., & Durrant-Whyte, H. (2006). Simultaneous localisation and mapping (SLAM): Part II state of the art. IEEE Robotics and Automation Magazine (RAM), 13(3), 108–117. doi:10.1109/MRA.2006.1678144.CrossRef

Bay, H., Ess, A., Tuytelaars, T., & van Gool, L. (2008). Speeded-up robust features (SURF). Computer Vision and Image Understanding (CVIU), 110(3), 346–359. doi:10.1016/j.cviu.2007.09.014.CrossRef

Cadena, C., Gálvez-López, D., Ramos, F., Tardós, J. D., & Neira, J. (2010). Robust place recognition with stereo cameras. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 5182–5189). doi:10.1109/IROS.2010.5650234.

Cadena, C., Gálvez-López, D., Tardós, J. D., & Neira, J. (2012). Robust place recognition with stereo sequences. IEEE Transactions on Robotics (TRO), 28(4), 871–885. doi:10.1109/TRO.2012.2189497.CrossRef

Calonder, M., Lepetit, V., Özuysal, M., Trzcinski, T., Strecha, C., & Fua, P. (2012). BRIEF: Computing a local binary descriptor very fast. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 34(7), 1281–1298. doi:10.1109/TPAMI.2011.222.CrossRef

Campos, F. M., Correia, L., & Calado, J. M. F. (2013). Loop closure detection with a holistic image feature. In Portuguese Conference on Artificial Intelligence (EPIA) (Vol. 8154, pp. 247–258). doi:10.1007/978-3-642-40669-0_22.

Caramazana, L., Arroyo, R., & Bergasa, L. M. (2016). Visual odometry correction based on loop closure detection. In: Open Conference on Future Trends in Robotics (RoboCity16) (pp. 97–104).

Carlevaris-Bianco, N., & Eustice, R. M. (2014). Learning visual feature descriptors for dynamic lighting conditions. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2769–2776). doi:10.1109/IROS.2014.6942941.

Carlevaris-Bianco, N., Ushani, A. K., & Eustice, R. M. (2016). University of Michigan North Campus long-term vision and lidar dataset. International Journal of Robotics Research (IJRR), 35(9), 1023–1035. doi:10.1177/0278364915614638.CrossRef

Ceriani, S., Fontana, G., Giusti, A., Marzorati, D., Matteucci, M., Migliore, D., et al. (2009). Rawseeds ground truth collection systems for indoor self-localization and mapping. Autonomous Robots, 27(4), 353–371. doi:10.1007/s10514-009-9156-5.CrossRef

Clemente, L. A., Davison, A. J., Reid, I. D., Neira, J., & Tardós, J. D. (2007). Mapping large loops with a single hand-held camera. In Robotics Science and Systems Conference (RSS) (pp. 297–304). doi:10.15607/RSS.2007.III.038.

Corke, P., Paul, R., Churchill, W., & Newman, P. (2013). Dealing with shadows: Capturing intrinsic scene appearance for image-based outdoor localisation. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2085–2092). doi:10.1109/IROS.2013.6696648.

Cummins, M., & Newman, P. (2008). FAB-MAP: Probabilistic localization and mapping in the space of appearance. International Journal of Robotics Research (IJRR), 27(6), 647–665. doi:10.1177/0278364908090961.CrossRef

Cummins, M., & Newman, P. (2010). Accelerating FAB-MAP with concentration inequalities. IEEE Transactions on Robotics (TRO), 26(6), 1042–1050. doi:10.1109/TRO.2010.2080390.CrossRef

Cummins, M., & Newman, P. (2010b). Appearance-only SLAM at large scale with FAB-MAP 2.0. International Journal of Robotics Research (IJRR), 30(9), 1100–1123. doi:10.1177/0278364910385483.CrossRef

Dalal, N., & Triggs, B. (2005). Histograms of oriented gradients for human detection. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (Vol. 2, pp. 886–893). doi:10.1109/CVPR.2005.177.

Drouilly, R., Rives, P., & Morisset, B. (2015) Semantic representation for navigation in large-scale environments. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 1106–1111). doi:10.1109/ICRA.2015.7139314.

Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localisation and mapping (SLAM): Part I the essential algorithms. IEEE Robotics and Automation Magazine (RAM), 13(2), 99–110. doi:10.1109/MRA.2006.1638022.CrossRef

Dymczyk, M., Lynen, S., Cieslewski, T., Bosse, M., Siegwart, R., & Furgale, P. (2015). The gist of maps—Summarizing experience for lifelong localization. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 2767–2773). doi:10.1109/ICRA.2015.7139575.

Erkent, O., & Bozma, H. I. (2015). Long-term topological place learning. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 5462–5467). doi:10.1109/ICRA.2015.7139962.

Fraundorfer, F., & Scaramuzza, D. (2012). Visual odometry—Part II: Matching, robustness, and applications. IEEE Robotics and Automation Magazine (RAM), 19(2), 78–90. doi:10.1109/MRA.2012.2182810.CrossRef

Fuentes-Pacheco, J., Ruiz-Ascencio, J., & Rendón-Mancha, J. M. (2012). Visual simultaneous localization and mapping: A survey. Artificial Intelligence Review (AIR). doi:10.1007/s10462-012-9365-8.

Gálvez-López, D., & Tardós, J. D. (2012). Bags of binary words for fast place recognition in image sequences. IEEE Transactions on Robotics (TRO), 28(5), 1188–1197. doi:10.1109/TRO.2012.2197158.CrossRef

Gao, X., & Zhang, T. (2017). Unsupervised learning to detect loops using deep neural networks for visual SLAM system. Autonomous Robots, 41(1), 1–18. doi:10.1007/s10514-015-9516-2.MathSciNetCrossRef

Garcia-Fidalgo, E., & Ortiz, A. (2015). Vision-based topological mapping and localization methods: A survey. Robotics and Autonomous Systems (RAS), 64, 1–20. doi:10.1016/j.robot.2014.11.009.CrossRef

Geiger, A., Roser, M., & Urtasun, R. (2010). Efficient large-scale stereo matching. In Asian Conference on Computer Vision (ACCV) (Vol. 6492, pp. 25–38). doi:10.1007/978-3-642-19315-6_3.

Geiger, A., Lenz, P., & Urtasun, R. (2012). Are we ready for autonomous driving? The KITTI vision benchmark suite. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 3354–3361). doi:10.1109/CVPR.2012.6248074.

Glover, A. J., Maddern, W., Milford, M., & Wyeth, G. F. (2010). FAB-MAP + RatSLAM: Appearance-based SLAM for multiple times of day. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 3507–3512). doi:10.1109/ROBOT.2010.5509547.

Glover, A. J., Maddern, W., Warren, M., Reid, S., Milford, M., & Wyeth, G. F. (2012). OpenFABMAP: An open source toolbox for appearance-based loop closure detection. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 4730–4735). doi:10.1109/ICRA.2012.6224843.

Hirschmuller, H. (2008). Stereo processing by semiglobal matching and mutual information. IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), 30(2), 328–341. doi:10.1109/TPAMI.2007.1166.CrossRef

Johns, E., & Yang, G. (2014). Generative methods for long-term place recognition in dynamic scenes. International Journal of Computer Vision (IJCV), 106(3), 297–314. doi:10.1007/s11263-013-0648-6.MathSciNetCrossRefMATH

Korrapati, H., & Mezouar, Y. (2017). Multi-resolution map building and loop closure with omnidirectional images. Autonomous Robots, 41(4), 967–987. doi:10.1007/s10514-016-9560-6.CrossRef

Korrapati, H., Uzer, F., & Mezouar, Y. (2013). Hierarchical visual mapping with omnidirectional images. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 3684–3690). doi:10.1109/IROS.2013.6696882.

Lee, G. H., & Pollefeys, M. (2014). Unsupervised learning of threshold for geometric verification in visual-based loop-closure. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 1510–1516). doi:10.1109/ICRA.2014.6907052.

Leutenegger, S., Chli, M., & Siegwart, R. Y. (2011). BRISK: Binary robust invariant scalable keypoints. In International Conference on Computer Vision (ICCV) (pp. 2548–2555). doi:10.1109/ICCV.2011.6126542.

Linegar, C., Churchill, W., & Newman, P. (2015). Work smart, not hard: Recalling relevant experiences for vast-scale but time-constrained localisation. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 90–97). doi:10.1109/ICRA.2015.7138985.

Liu, Y., & Zhang, H. (2012). Visual loop closure detection with a compact image descriptor. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1051–1056). doi:10.1109/IROS.2012.6386145.

Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision (IJCV), 60(2), 91–110. doi:10.1023/B:VISI.0000029664.99615.94.CrossRef

Lowry, S., & Milford, M. (2015). Change removal: Robust online learning for changing appearance and changing viewpoint. In Workshop on Visual Place Recognition in Changing Environments at the IEEE International Conference on Robotics and Automation (W-ICRA).

Lowry, S., Sünderhauf, N., Newman, P., Leonard, J. J., Cox, D., Corke, P., et al. (2016). Visual place recognition: A survey. IEEE Transactions on Robotics (TRO), 32(1), 1–19. doi:10.1109/TRO.2015.2496823.CrossRef

Lv, Q., Josephson, W., Wang, Z., Charikar, M., & Li, K. (2007). Multi-probe LSH: Efficient indexing for high-dimensional similarity search. In International Conference on Very Large Data Bases (VLDB) (pp. 950–961).

Masatoshi, A., Yuuto, C., Kanji, T., & Kentaro, Y. (2015). Leveraging image-based prior in cross-season place recognition. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 5455–5461). doi:10.1109/ICRA.2015.7139961.

McManus, C., Churchill, W., Maddern, W., Stewart, A., & Newman, P. (2014). Shady dealings: Robust, long-term visual localisation using illumination invariance. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 901–906). doi:10.1109/ICRA.2014.6906961.

Milford, M. (2012). Visual route recognition with a handful of bits. In Robotics Science and Systems Conference (RSS) (pp. 297–304). doi:10.15607/RSS.2012.VIII.038.

Milford, M., & Wyeth, G. F. (2012). SeqSLAM: Visual route-based navigation for sunny summer days and stormy winter nights. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 1643–1649). doi:10.1109/ICRA.2012.6224623.

Mohan, M., Gálvez-López, D., Monteleoni, C., & Sibley, G. (2015). Environment selection and hierarchical place recognition. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 5487–5494). doi:10.1109/ICRA.2015.7139966.

Mousavian, A., Kosecká, J., & Lien, J. (2015). Semantically guided location recognition for outdoors scenes. In IEEE International Conference on Robotics and Automation (ICRA). (pp. 4882–4889). doi:10.1109/ICRA.2015.7139877.

Muja, M., & Lowe, D. G. (2012). Fast matching of binary features. In Canadian Conference on Computer and Robot Vision (CRV) (pp. 404–410). doi:10.1109/CRV.2012.60.

Muja, M., & Lowe, D. G. (2014). Scalable nearest neighbor algorithms for high dimensional data. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 36(11), 2227–2240. doi:10.1109/TPAMI.2014.2321376.CrossRef

Mur-Artal, R., Montiel, J. M. M., & Tardós, J. D. (2015). ORB-SLAM: A versatile and accurate monocular SLAM system. IEEE Transactions on Robotics (TRO), 31(5), 1147–1163. doi:10.1109/TRO.2015.2463671.CrossRef

Murillo, A. C., Singh, G., Kosecká, J., & Guerrero, J. J. (2013). Localization in urban environments using a panoramic gist descriptor. IEEE Transactions on Robotics (TRO), 29(1), 146–160. doi:10.1109/TRO.2012.2220211.CrossRef

Negre-Carrasco, P. L., Bonin-Font, F., & Oliver-Codina, G. (2016). Global image signature for visual loop-closure detection. Autonomous Robots, 40(8), 1403–1417. doi:10.1007/s10514-015-9522-4.CrossRef

Nelson, P., Churchill, W., Posner, I., & Newman, P. (2015). From dusk till dawn: Localisation at night using artificial light sources. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 5245–5252). doi:10.1109/ICRA.2015.7139930.

Neubert, P., Sünderhauf, N., & Protzel, P. (2015). Superpixel-based appearance change prediction for long-term navigation across seasons. Robotics and Autonomous Systems (RAS), 69(7), 15–27. doi:10.1016/j.robot.2014.08.005.CrossRef

Ojala, T., Pietikäinen, M., & Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition (PR), 29(1), 51–59. doi:10.1016/0031-3203(95)00067-4.CrossRef

Oliva, A., & Torralba, A. (2006). Building the gist of a scene: The role of global image features in recognition. Visual Perception, Progress in Brain Research (PBR), 155(B), 23–36. doi:10.1016/S0079-6123(06)55002-2.

Pandey, G., McBride, J. R., & Eustice, R. (2011). Ford Campus vision and lidar data set. International Journal of Robotics Research (IJRR), 30(13), 1543–1552. doi:10.1177/0278364911400640.CrossRef

Paul, R., & Newman, P. (2010). FAB-MAP 3D: Topological mapping with spatial and visual appearance. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 2649–2656). doi:10.1109/ROBOT.2010.5509587.

Pepperell, E., Corke, P., & Milford, M. (2014). All-environment visual place recognition with SMART. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 1612–1618). doi:10.1109/ICRA.2014.6907067.

Pepperell, E., Corke, P., & Milford, M. (2015). Automatic image scaling for place recognition in changing environments. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 1118–1124). doi:10.1109/ICRA.2015.7139316.

Rublee, E., Rabaud, V., Konolige, K., & Bradski, G. (2011). ORB: An efficient alternative to SIFT or SURF. In International Conference on Computer Vision (ICCV) (pp. 2564–2571). doi:10.1109/ICCV.2011.6126544.

Scaramuzza, D., & Fraundorfer, F. (2011). Visual Odometry—Part I: The first 30 years and fundamentals. IEEE Robotics and Automation Magazine (RAM), 18(4), 80–92. doi:10.1109/MRA.2011.943233.CrossRef

Smith, M., Baldwin, I., Churchill, W., Paul, R., & Newman, P. (2009). The New College vision and laser data set. International Journal of Robotics Research (IJRR), 28(5), 595–599. doi:10.1177/0278364909103911.CrossRef

Sünderhauf, N., & Protzel, P. (2011). BRIEF-Gist—Closing the loop by simple means. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1234–1241). doi:10.1109/IROS.2011.6094921.

Sünderhauf, N., Neubert, P., & Protzel, P. (2013). Are we there yet? Challenging SeqSLAM on a 3000 km journey across all four seasons. In Workshop on Long-Term Autonomy at the IEEE International Conference on Robotics and Automation (W-ICRA).

Sünderhauf, N., Shirazi, S., Jacobson, A., Dayoub, F., Pepperell, E., Upcroft, B., & Milford, M. (2015). Place recognition with ConvNet landmarks: Viewpoint-robust, condition-robust, training-free. In Robotics Science and Systems Conference (RSS) (pp. 1–10). doi:10.15607/RSS.2015.XI.022.

Ulrich, I., & Nourbakhsh, I. R. (2000) Appearance-based place recognition for topological localization. In IEEE International Conference on Robotics and Automation (ICRA). (pp. 1023–1029). doi:10.1109/ROBOT.2000.844734.

Upcroft, B., McManus, C., Churchill, W., Maddern, W., & Newman, P. (2014). Lighting invariant urban street classification. In IEEE International Conference on Robotics and Automation (ICRA) (pp. 1712–1718). doi:10.1109/ICRA.2014.6907082.

Valgren, C., & Lilienthal, A. J. (2010). SIFT, SURF and seasons: Appearance-based long-term localization in outdoor environments. Robotics and Autonomous Systems (RAS), 58(2), 149–156. doi:10.1016/j.robot.2009.09.010.CrossRef

Williams, B., Cummins, M., Neira, J., Newman, P., Reid, I. D., & Tardós, J. D. (2008). An image-to-map loop closing method for monocular SLAM. In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 2053–2059). doi:10.1109/IROS.2008.4650996.

Williams, B., Cummins, M., Neira, J., Newman, P., Reid, I. D., & Tardós, J. D. (2009). A comparison of loop closing techniques in monocular SLAM. Robotics and Autonomous Systems (RAS), 57(12), 1188–1197. doi:10.1016/j.robot.2009.06.010.CrossRef

Yang, X., & Cheng, K. T. (2014). Local difference binary for ultrafast and distinctive feature description. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 36(1), 188–194. doi:10.1109/TPAMI.2013.150.CrossRef

Titel: Are you ABLE to perform a life-long visual topological localization?
verfasst von: Roberto Arroyo
Pablo F. Alcantarilla
Luis M. Bergasa
Eduardo Romera
Publikationsdatum: 26.07.2017
Verlag: Springer US
Erschienen in: Autonomous Robots / Ausgabe 3/2018
Print ISSN: 0929-5593
Elektronische ISSN: 1573-7527
DOI: https://doi.org/10.1007/s10514-017-9664-7

Neuer Inhalt

Bildnachweise

VDI-Icon, Profil Icon, inhalt2, Springer Professional Modul/© Springer Fachmedien Wiesbaden GmbH, Nachhaltigkeitsaward Key Visual/© Cometis AG/Global ESG Monitor | Daniel Rupp | Generiert mit KI, Search Icon, Banner Hanser, Jonas Klose/© Pine Valley Capital GmbH, Carina Kießling von der Strategieberatung Roland Berger/© Monika Walther Fotografie | ATZ, Beijing Auto Show 2024: Deutsche Hersteller wollen angreifen./© EKH-Pictures / Generated with AI / Stock.adobe.com, Zeitschrift Wissensmanagement Cover, PatentFit-Logo/© Springer Fachmedien Wiesbaden GmbH, Zukunftswerkstatt Sales Excellence 2024/© AndreyPopov / Getty Images / iStock, 2023_Antrieb/© supervisuell, ATZ-Webinar: Prototypenfreie Entwicklung durch Offline- und Driver-in-the-Loop-HiL-Tests /© (c) VI-grade

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 3/2018

Integration of sliding mode based steering control and PSO based drive force control for a 4WS4WD vehicle

Relative motion estimation using visual–inertial optical flow

Unsupervised early prediction of human reaching for human–robot collaboration in shared workspaces

Using probabilistic movement primitives in robotics

A kernel-based approach to learning contact distributions for robot manipulation tasks

Range-only SLAM for robot-sensor network cooperation

Neuer Inhalt

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.