Automatic translation of human route descriptions into schematic maps for indoor navigation

References

• Belouaer, L., Brosset, D., & Claramunt, C. (2013). Modeling spatial knowledge from verbal descriptions. In T. Tenbrink, S. J. A. Galton, & Z. Wood (Eds.), Spatial information theory. COSIT 2013. Lecture notes in computer science (Vol. 8116, pp. 338–357). Springer. https://doi.org/10.1007/978-3-319-01790-7_19
   Google Scholar
• Belouaer, L., Brosset, D., & Claramunt, C. (2016). From verbal route descriptions to sketch maps in natural environments. In M. Ali, S. Newsam, M. Renz, G. Trajcevski, & S. Ravada (Eds.), Proceedings of the 24th ACM SIGSPATIAL international conference on advances in geographic information systems (pp. 1–10). Association for Computing Machinery. https://doi.org/10.1145/2996913.2997003
   Google Scholar
• Brosset, D., Claramunt, C., & Saux, E. (2007). A location and action-based model for route descriptions. In F. T. Fonseca, M. A. Rodríguez, & S. Levashkin (Eds.), GeoSpatial semantics. GeoS 2007. Lecture notes in computer science (Vol. 4853, pp. 146–159). Springer. https://doi.org/10.1007/978-3-540-76876-0_10
   Google Scholar
• Chen, H., Vasardani, M., Winter, S., & Tomko, M. (2018). A graph database model for knowledge extracted from place descriptions. ISPRS International Journal of Geo-Information, 7(221), 1–30. https://doi.org/10.3390/ijgi7060221
   Google Scholar
• Chen, J., & Clarke, K. (2020). Indoor cartography. Cartography and Geographic Information Science, 47(2), 95–109. https://doi.org/10.1080/15230406.2019.1619482
   Web of Science ®Google Scholar
• Clark, J. (2023). Pillow (PIL Fork) documentation. Read the Docs. https://buildmedia.readthedocs.org/media/pdf/pillow/latest/pillow.pdf
   Google Scholar
• Freksa, C., Moratz, R., & Barkowsky, T. (2000). Schematic maps for robot navigation. In C. Freksa, C. Habel, W. Brauer, & K. F. Wender (Eds.), Spatial cognition II - Integrating abstract theories, empirical studies, formal models, and practical applications (Vols. LNAI, 1849, pp. 100–114). Springer. https://doi.org/10.1007/3-540-45460-8_8
   Google Scholar
• Galvão, M. L., Krukar, J., & Schwering, A. (2021). Evaluating schematic route maps in wayfinding tasks for in-car navigation. Cartography and Geographic Information Science, 48(5), 449–469. https://doi.org/10.1080/15230406.2021.1943531
   PubMed Web of Science ®Google Scholar
• Gartner, G., & Radoczky, V. (2005). Schematic vs. topographic maps in pedestrian navigation: How much map detail is necessary to support wayfinding. In T. Barkowsky, C. Freksa, M. Hegarty, & R. K. Lowe (Eds.), AAAI Spring symposium: Reasoning with mental and external diagrams: Computational modeling and spatial assistance (pp. 41–47). AAAI.
   Google Scholar
• Guan, F., Fang, Z., Zhang, X., Zhong, H., Zhang, J., & Huang, H. (2023). Using street-view panoramas to model the decision-making complexity of road intersections based on the passing branches during navigation. Computers, Environment and Urban Systems, 103, 1–18. https://doi.org/10.1016/j.compenvurbsys.2023.101975
   Web of Science ®Google Scholar
• Honnibal, M., & Johnson, M. (2015). An improved non-monotonic transition system for dependency parsing. In L. Màrquez, C. Callison-Burch, & J. Su (Eds.), Proceedings of the 2015 conference on empirical methods in natural language processing (pp. 1373–1378). Association for Computational Linguistics. https://doi.org/10.18653/v1/D15-1162
   Google Scholar
• Honnibal, M., Montani, I., Van Landeghem, S., & Boyd, A. (2020). spaCy: Industrial-strength natural language processing in Python. Zenodo. https://doi.org/10.5281/zenodo.1212303
   Google Scholar
• Jurafsky, D., & Martin, J. (2014). Speech and language processing (2nd ed.). Prentice Hall.
   Google Scholar
• Khan, A., Vasardani, M., & Winter, S. (2013). Extracting spatial information from place descriptions. COMP ’13: Proceedings of the first ACM SIGSPATIAL international workshop on computational models of place (pp. 62–69). Association for Computing Machinery. https://doi.org/10.1145/2534848.2534857
   Google Scholar
• Kim, J., Vasardani, M., & Winter, S. (2016). From descriptions to depictions: A dynamic sketch map drawing strategy. Spatial Cognition & Computation, 16(1), 29–53. https://doi.org/10.1080/13875868.2015.1084509
   Web of Science ®Google Scholar
• Klippel, A., Tappe, H., Kulik, L., & Lee, P. U. (2005). Wayfinding choremes—a language for modeling conceptual route knowledge. Journal of Visual Languages & Computing, 16(4), 311–329. https://doi.org/10.1016/j.jvlc.2004.11.004
   Web of Science ®Google Scholar
• Kuipers, B. (1982). The “map in the head” metaphor. Environment and Behavior, 14(2), 202–220. https://doi.org/10.1177/0013916584142005
   Web of Science ®Google Scholar
• Lan, T., Li, Z., Wang, J., Gong, C., & Ti, P. (2022). An ANNs-based method for automated labelling of schematic metro maps. ISPRS International Journal of Geo-Information, 11(1), 1–20. https://doi.org/10.3390/ijgi11010036
   Web of Science ®Google Scholar
• Lane, H., Howard, C., & Hapke, H. M. (2019). Natural language processing in action understanding, analyzing, and generating text with Python. Manning Publications.
   Google Scholar
• Leech, G., Deuchar, M., & Hoogenraad, R. (2005). English grammar for today: A new introduction (2nd ed.). Red Globe Press. https://doi.org/10.1007/978-1-349-16878-1
   Google Scholar
• Liu, F., Vasardani, M., & Baldwin, T. (2014). Automatic identification of locative expressions from social media text: A comparative analysis. In D. Ahlers, E. Wilde, & B. Martins (Eds.), LocWeb ’14: Proceedings of the 4th international workshop on location and the web (pp. 9–16). Association for Computing Machinery. https://doi.org/10.1145/2663713.2664426
   Google Scholar
• Matuszek, C., Herbst, E. Z., & Fox, D. (2013). Learning to parse natural language commands to a robot control system. Experimental Robotics, 403–415. https://doi.org/10.1007/978-3-319-00065-7_28
   Google Scholar
• McKinney, W. (2010). Data structures for statistical computing in python. In S. van der Walt & J. Millman (Eds.), Proceedings of the 9th python in science conference (pp. 51–56). SciPy. https://doi.org/10.25080/Majora-92bf1922-00a
   Google Scholar
• Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. arXiv:1310.4546. https://doi.org/10.48550/arXiv.1310.4546
   Google Scholar
• Murai, Y., Okada, M., & Hashimoto, K. (2011). Characteristics of human route descriptions and their improvement by machine landmark extraction. 2011 3rd international conference on awareness science and technology (iCAST) (pp. 438–443). IEEE. https://doi.org/10.1109/ICAwST.2011.6163115
   Google Scholar
• Nivre, J. (2003). An efficient algorithm for projective dependency parsing. In Proceedings of the Eighth International Conference on Parsing Technologies (pp. 149–160). Ann Arbor, Michigan, US.
   Google Scholar
• Nivre, J., & Nilsson, J. (2005). Pseudo-projective dependency parsing. In K. Knight (Ed.), Proceedings of the 43rd annual meeting of the ACL (pp. 99–106). Association for Computational Linguistics. https://doi.org/10.3115/1219840.1219853
   Google Scholar
• Ooms, K., Duytschaever, A., Stroeken, K., Verdoolaege, A., Viaene, P., & Van de Weghe, N. (2019). Fine-tuning the usability of a crowdsourced indoor navigation system. Cartography and Geographic Information Science, 46(5), 456–473. https://doi.org/10.1080/15230406.2018.1535331
   Web of Science ®Google Scholar
• Pandas Development Team. (n.d.). pandas-dev/pandas: Pandas. https://doi.org/10.5281/zenodo.3509134
   Google Scholar
• Python Software Foundation. (n.d.-a.). Re - regular expression operations. https://docs.python.org/3/library/re.html
   Google Scholar
• Python Software Foundation. (n.d.-b.). Tkinter — Python interface to Tcl/tk. https://docs.python.org/3/library/tkinter.html
   Google Scholar
• Ti, P., Wu, H., Li, Z., Li, M., Dai, R., & Xiong, T. (2023). Revealing schematic map designs with preservation of relativity in node position and segment length in existing official maps. ISPRS International Journal of Geo-Information, 12(309), 1–28. https://doi.org/10.3390/ijgi12080309
   Google Scholar
• Vasardani, M., Timpf, S., Winter, S., & Tomko, M. (2013). From descriptions to depictions: A conceptual framework. In T. Tenbrink, J. Stell, A. Galton, & Z. Wood (Eds.), Spatial information theory. COSIT 2013. Lecture notes in computer science (Vol. 8116, pp. 299–319). Springer. https://doi.org/10.1007/978-3-319-01790-7_17
   Google Scholar
• Werner, S., Krieg-Brückner, B., & Herrmann, T. (2000). Modelling navigational knowledge by route graphs. In C. Freksa, C. Habel, W. Brauer, & K. F. Wender (Eds.), Spatial cognition II - Integrating abstract theories, empirical studies, formal models, and practical applications (Vol. 1849, pp. 295–316). Springer. https://doi.org/10.1007/3-540-45460-8_22
   Google Scholar
• Winter, S., Hamzei, E., Van de Weghe, N., & Ooms, K. (2018). A graph representation for verbal indoor route descriptions. In S. Creem-Regehr, J. Schöning, & A. Klippel (Eds.), 11th international conference, spatial cognition 2018 (Vol. 11034, pp. 77–91). Springer. https://doi.org/10.1007/978-3-319-96385-3_6
   Google Scholar
• Zang, X., Pokle, A., Vázquez, M., Chen, K., Niebles, J. C., Soto, A., & Savarese, S. (2018). Translating navigation instructions in natural language to a high-level plan for behavioral robot navigation. arXiv:1810.00663. https://doi.org/10.48550/arXiv.1810.00663
   Google Scholar
• Zhang, X., Mitra, P., Klippel, A., & MacEachren, A. (2010). Automatic extraction of destinations, origins and route parts from human generated route directions. In S. I. Fabrikant, T. Reichenbacher, M. van Kreveld, & C. Schlieder (Eds.), Geographic information science. GIScience 2010. Lecture notes in computer science (Vol. 6292, pp. 279–294). Springer. https://doi.org/10.1007/978-3-642-15300-6_20
   Google Scholar