{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,5,7]],"date-time":"2023-05-07T04:21:36Z","timestamp":1683433296544},"reference-count":31,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. & Syst."],"published-print":{"date-parts":[[2023,5,1]]},"DOI":"10.1587\/transinf.2022dlp0033","type":"journal-article","created":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T22:24:05Z","timestamp":1682893445000},"page":"756-764","source":"Crossref","is-referenced-by-count":0,"title":["Semantic Path Planning for Indoor Navigation Tasks Using Multi-View Context and Prior Knowledge"],"prefix":"10.1587","volume":"E106.D","author":[{"given":"Jianbing","family":"WU","sequence":"first","affiliation":[{"name":"Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weibo","family":"HUANG","sequence":"additional","affiliation":[{"name":"Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoliang","family":"HUA","sequence":"additional","affiliation":[{"name":"Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wanruo","family":"ZHANG","sequence":"additional","affiliation":[{"name":"Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Risheng","family":"KANG","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, KU Leuven"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hong","family":"LIU","sequence":"additional","affiliation":[{"name":"Key Laboratory of Machine Perception, Shenzhen Graduate School, Peking University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] L.E. Kavraki, P. Svestka, J.-C. Latombe, and M.H. Overmars, \u201cProbabilistic roadmaps for path planning in high-dimensional configuration spaces,\u201d IEEE Trans. Robot. Autom., vol.12, no.4, pp.566-580, Aug. 1996. 10.1109\/70.508439","DOI":"10.1109\/70.508439"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] S.M. LaValle and J.J. Kuffner, Jr., \u201cRapidly-Exploring Random Trees: Progress and Prospects,\u201d in Algorithmic and Computational Robotics, ed. B. Donald, K. Lynch, and D. Rus, pp.303-307, A K Peters\/CRC Press, zeroth ed., April 2001. 10.1201\/9781439864135-43","DOI":"10.1201\/9781439864135-43"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] Y. Zhu, R. Mottaghi, E. Kolve, J.J. Lim, A. Gupta, L. Fei-Fei, and A. Farhadi, \u201cTarget-driven visual navigation in indoor scenes using deep reinforcement learning,\u201d 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, Singapore, pp.3357-3364, IEEE, May 2017. 10.1109\/icra.2017.7989381","DOI":"10.1109\/ICRA.2017.7989381"},{"key":"4","doi-asserted-by":"publisher","unstructured":"[4] J. Kulh\u00e1nek, E. Derner, and R. Babu\u0161ka, \u201cVisual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement Learning,\u201d arXiv:2010.10903 [cs], Oct. 2020. 10.1109\/lra.2021.3068106","DOI":"10.1109\/LRA.2021.3068106"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] A. Devo, G. Mezzetti, G. Costante, M.L. Fravolini, and P. Valigi, \u201cTowards Generalization in Target-Driven Visual Navigation by Using Deep Reinforcement Learning,\u201d IEEE Trans. Robot., vol.36, no.5, pp.1546-1561, Oct. 2020. 10.1109\/tro.2020.2994002","DOI":"10.1109\/TRO.2020.2994002"},{"key":"6","doi-asserted-by":"publisher","unstructured":"[6] R. Druon, Y. Yoshiyasu, A. Kanezaki, and A. Watt, \u201cVisual Object Search by Learning Spatial Context,\u201d IEEE Robotics and Automation Letters, vol.5, no.2, pp.1279-1286, April 2020. 10.1109\/lra.2020.2967677","DOI":"10.1109\/LRA.2020.2967677"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] A. Mousavian, A. Toshev, M. Fi\u0161er, J. Ko\u0161eck\u00e1, A. Wahid, and J. Davidson, \u201cVisual representations for semantic target driven navigation,\u201d 2019 International Conference on Robotics and Automation (ICRA), pp.8846-8852, IEEE, 2019. 10.1109\/icra.2019.8793493","DOI":"10.1109\/ICRA.2019.8793493"},{"key":"8","doi-asserted-by":"crossref","unstructured":"[8] D. Liu, Y. Cui, Z. Cao, and Y. Chen, \u201cIndoor Navigation for Mobile Agents: A Multimodal Vision Fusion Model,\u201d 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom, pp.1-8, IEEE, July 2020. 10.1109\/ijcnn48605.2020.9207265","DOI":"10.1109\/IJCNN48605.2020.9207265"},{"key":"9","unstructured":"[9] W. Yang, X. Wang, A. Farhadi, A. Gupta, and R. Mottaghi, \u201cVisual semantic navigation using scene priors,\u201d Proceedings of Seventh International Conference on Learning Representations (ICLR 2019), May 2019."},{"key":"10","unstructured":"[10] Y. Qiu, A. Pal, and H.I. Christensen, \u201cLearning hierarchical relationships for object-goal navigation,\u201d 2020 Conference on Robot Learning (CoRL), 2020."},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] T.-L. Nguyen, D.-V. Nguyen, and T.-H. Le, \u201cReinforcement Learning Based Navigation with Semantic Knowledge of Indoor Environments,\u201d 2019 11th International Conference on Knowledge and Systems Engineering (KSE), Da Nang, Vietnam, pp.1-7, IEEE, Oct. 2019. 10.1109\/kse.2019.8919366","DOI":"10.1109\/KSE.2019.8919366"},{"key":"12","unstructured":"[12] T.N. Kipf and M. Welling, \u201cSemi-supervised classification with graph convolutional networks,\u201d 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, OpenReview.net, 2017."},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. Reid, and J.J. Leonard, \u201cPast, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age,\u201d IEEE Trans. Robot., vol.32, no.6, pp.1309-1332, Dec. 2016. 10.1109\/tro.2016.2624754","DOI":"10.1109\/TRO.2016.2624754"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] P. Hart, N. Nilsson, and B. Raphael, \u201cA Formal Basis for the Heuristic Determination of Minimum Cost Paths,\u201d IEEE Trans. Syst. Sci. Cybern., vol.4, no.2, pp.100-107, 1968. 10.1109\/tssc.1968.300136","DOI":"10.1109\/TSSC.1968.300136"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] A. Kanezaki, J. Nitta, and Y. Sasaki, \u201cGOSELO: Goal-Directed Obstacle and Self-Location Map for Robot Navigation Using Reactive Neural Networks,\u201d IEEE Robotics and Automation Letters, vol.3, no.2, pp.696-703, April 2018. 10.1109\/lra.2017.2783400","DOI":"10.1109\/LRA.2017.2783400"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] K. Ota, Y. Sasaki, D.K. Jha, Y. Yoshiyasu, and A. Kanezaki, \u201cEfficient Exploration in Constrained Environments with Goal-Oriented Reference Path,\u201d 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, pp.6061-6068, IEEE, Oct. 2020. 10.1109\/iros45743.2020.9341620","DOI":"10.1109\/IROS45743.2020.9341620"},{"key":"17","unstructured":"[17] P. Anderson, A. Chang, D.S. Chaplot, A. Dosovitskiy, S. Gupta, V. Koltun, J. Kosecka, J. Malik, R. Mottaghi, M. Savva, and A.R. Zamir, \u201cOn evaluation of embodied navigation agents,\u201d arXiv preprint arXiv:1807.06757 [cs], July 2018."},{"key":"18","doi-asserted-by":"publisher","unstructured":"[18] Y. Sun, M. Liu, and M.Q.-H. Meng, \u201cImproving RGB-D SLAM in dynamic environments: A motion removal approach,\u201d Robotics and Autonomous Systems, vol.89, pp.110-122, March 2017. 10.1016\/j.robot.2016.11.012","DOI":"10.1016\/j.robot.2016.11.012"},{"key":"19","doi-asserted-by":"publisher","unstructured":"[19] Y. Sun, M. Liu, and M.Q.-H. Meng, \u201cMotion removal for reliable RGB-D SLAM in dynamic environments,\u201d Robotics and Autonomous Systems, vol.108, pp.115-128, Oct. 2018. 10.1016\/j.robot.2018.07.002","DOI":"10.1016\/j.robot.2018.07.002"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] D. Silver, A. Huang, C.J. Maddison, A. Guez, L. Sifre, G. van den Driessche, J. Schrittwieser, I. Antonoglou, V. Panneershelvam, M. Lanctot, S. Dieleman, D. Grewe, J. Nham, N. Kalchbrenner, I. Sutskever, T. Lillicrap, M. Leach, K. Kavukcuoglu, T. Graepel, and D. Hassabis, \u201cMastering the game of Go with deep neural networks and tree search,\u201d Nature, vol.529, no.7587, pp.484-489, Jan. 2016. 10.1038\/nature16961","DOI":"10.1038\/nature16961"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] D. Silver, J. Schrittwieser, K. Simonyan, I. Antonoglou, A. Huang, A. Guez, T. Hubert, L. Baker, M. Lai, A. Bolton, Y. Chen, T. Lillicrap, F. Hui, L. Sifre, G. van den Driessche, T. Graepel, and D. Hassabis, \u201cMastering the game of Go without human knowledge,\u201d Nature, vol.550, no.7676, pp.354-359, Oct. 2017. 10.1038\/nature24270","DOI":"10.1038\/nature24270"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] X. Ye, Z. Lin, H. Li, S. Zheng, and Y. Yang, \u201cActive Object Perceiver: Recognition-Guided Policy Learning for Object Searching on Mobile Robots,\u201d 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, pp.6857-6863, IEEE, Oct. 2018. 10.1109\/iros.2018.8593720","DOI":"10.1109\/IROS.2018.8593720"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] Q. Wu, X. Gong, K. Xu, D. Manocha, J. Dong, and J. Wang, \u201cTowards Target-Driven Visual Navigation in Indoor Scenes via Generative Imitation Learning,\u201d IEEE Robotics and Automation Letters, vol.6, no.1, pp.175-182, Jan. 2021. 10.1109\/lra.2020.3036597","DOI":"10.1109\/LRA.2020.3036597"},{"key":"24","unstructured":"[24] V. Mnih, A.P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, \u201cAsynchronous methods for deep reinforcement learning,\u201d Proceedings of the 33rd International Conference on Machine Learning, ed. M.F. Balcan and K.Q. Weinberger, Proceedings of Machine Learning Research, vol.48, pp.1928-1937, June 2016."},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep Residual Learning for Image Recognition,\u201d 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, pp.770-778, IEEE, June 2016. 10.1109\/cvpr.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"26","doi-asserted-by":"crossref","unstructured":"[26] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, \u201cImageNet: A large-scale hierarchical image database,\u201d 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, FL, pp.248-255, IEEE, June 2009. 10.1109\/cvpr.2009.5206848","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] J. Pennington, R. Socher, and C. Manning, \u201cGlove: Global Vectors for Word Representation,\u201d Proc. 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp.1532-1543, Association for Computational Linguistics, 2014. 10.3115\/v1\/d14-1162","DOI":"10.3115\/v1\/D14-1162"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalantidis, L.-J. Li, D.A. Shamma, M.S. Bernstein, and L. Fei-Fei, \u201cVisual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations,\u201d International Journal of Computer Vision, vol.123, no.1, pp.32-73, May 2017. 10.1007\/s11263-016-0981-7","DOI":"10.1007\/s11263-016-0981-7"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] M. Wortsman, K. Ehsani, M. Rastegari, A. Farhadi, and R. Mottaghi, \u201cLearning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning,\u201d 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, pp.6743-6752, IEEE, June 2019. 10.1109\/cvpr.2019.00691","DOI":"10.1109\/CVPR.2019.00691"},{"key":"30","unstructured":"[30] E. Kolve, R. Mottaghi, W. Han, E. VanderBilt, L. Weihs, A. Herrasti, D. Gordon, Y. Zhu, A. Gupta, and A. Farhadi, \u201cAI2-THOR: An interactive 3D environment for visual AI,\u201d arXiv, 2017."},{"key":"31","unstructured":"[31] D.P. Kingma and J. Ba, \u201cAdam: A method for stochastic optimization,\u201d 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings, ed. Y. Bengio and Y. LeCun, 2015."}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/5\/E106.D_2022DLP0033\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,6]],"date-time":"2023-05-06T04:16:58Z","timestamp":1683346618000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E106.D\/5\/E106.D_2022DLP0033\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,1]]},"references-count":31,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2022dlp0033","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"value":"0916-8532","type":"print"},{"value":"1745-1361","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,1]]},"article-number":"2022DLP0033"}}