Automatic Construction of Floor Plan with Smartphone Sensors
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摘要
Indoor floor plans are of vital importance for a wide range of indoor social applications, however, they are often unavailable due to various reasons. This paper proposes a method to automatically construct the indoor floor plan with rooms and corridors as well as landmarks, using inertial traces collected with smartphones. Landmarks,such as turnings, doors, and stairs, are identified according to inertial sensors and WiFi signals. The inertial traces are then partitioned into segments according to the turnings and classified as room type or corridor type according to the doors. Clustering is applied on room type and corridor type trace segments separately to produce room type and corridor type clusters. The construction of room applies the α-shape algorithm on room type clusters and the construction of corridor employs the principal component analysis(PCA) algorithm or the α-shape algorithm on corridor type clusters. Evaluations in two representative scenarios show that the method can construct the floor plan of acceptable accuracy with a relatively small set of inertial traces.
Indoor floor plans are of vital importance for a wide range of indoor social applications, however, they are often unavailable due to various reasons. This paper proposes a method to automatically construct the indoor floor plan with rooms and corridors as well as landmarks, using inertial traces collected with smartphones. Landmarks,such as turnings, doors, and stairs, are identified according to inertial sensors and WiFi signals. The inertial traces are then partitioned into segments according to the turnings and classified as room type or corridor type according to the doors. Clustering is applied on room type and corridor type trace segments separately to produce room type and corridor type clusters. The construction of room applies the α-shape algorithm on room type clusters and the construction of corridor employs the principal component analysis(PCA) algorithm or the α-shape algorithm on corridor type clusters. Evaluations in two representative scenarios show that the method can construct the floor plan of acceptable accuracy with a relatively small set of inertial traces.
Indoor floor plans are of vital importance for a wide range of indoor social applications, however, they are often unavailable due to various reasons. This paper proposes a method to automatically construct the indoor floor plan with rooms and corridors as well as landmarks, using inertial traces collected with smartphones. Landmarks,such as turnings, doors, and stairs, are identified according to inertial sensors and WiFi signals. The inertial traces are then partitioned into segments according to the turnings and classified as room type or corridor type according to the doors. Clustering is applied on room type and corridor type trace segments separately to produce room type and corridor type clusters. The construction of room applies the α-shape algorithm on room type clusters and the construction of corridor employs the principal component analysis(PCA) algorithm or the α-shape algorithm on corridor type clusters. Evaluations in two representative scenarios show that the method can construct the floor plan of acceptable accuracy with a relatively small set of inertial traces.
引文
[1] Y. Xuan,R. Sengupta,and Y. Fallah,"Making indoor maps with portable accelerometer and magnetometer,"in Proc.of Ubiquitous Positioning Indoor Navigation and Location Based Service, 2010, pp. 1-7.
[2] D. Shin, Y. Chon, and H. Cha,"Unsupervised construction of an indoor floor plan using a smartphone,"IEEE Trans.on Systems, Man, and Cybernetics, Part C(Applications and Reviews), vol. 42, no. 6, pp. 889-898, 2012.
[3] G. Shen, Z. Chen, P. Zhang, T. Moscibroda, and Y. Zhang,"Walkie-Markie:Indoor pathway mapping made easy,"in Proc. of the 10th USENIX Conf. on Networked Systems Design and Implementation, 2013, pp. 85-98.
[4] X. Zhang, Y. Jin, H.-X. Tan, and W.-S. Soh,"CIMLoc:A crowdsourcing indoor digital map construction system for localization,"in Proc. of the IEEE 9th Intl. Conf. on Intelligent Sensors, Sensor Networks and Information Processing,2014, pp. 1-6.
[5] P. Mazumdar, V. J. Ribeiro, and S. Tewari,"Generating indoor maps by crowdsourcing positioning data from smartphones,"in Proc. of Intl. Conf. on Indoor Positioning and Indoor Navigation, 2014, pp. 322-331.
[6] B. Zhou, Q. Li, Q. Mao, W. Tu, X. Zhang, and L. Chen,"ALIMC:Activity landmark-based indoor mapping via crowdsourcing,"IEEE Trans. on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2774-2785, 2015.
[7] M. Alzantot and M. Youssef,"Crowdlnside:Automatic construction of indoor floorplans,"in Proc. of the 20th Intl.Conf. on Advances in Geographic Information Systems, 2012, pp. 99-108.
[8] Y. Jiang,Y. Xiang,X. Pan,et al.,"Hallway based automatic indoor floorplan construction using room fingerprints,"in Proc. of the ACM Intl. Joint Conf. on Pervasive and Ubiquitous Computing, 2013, pp. 315-324.
[9] D. Philipp, P. Baier, C. Dibak, et al.,"MapGENIE:Grammar-enhanced indoor map construction from crowd-sourced data,"in Proc. of IEEE Intl. Conf. on Pervasive Computing and Communications, 2014, pp. 139-147.
[10] R. Zhou,"Pedestrian dead reckoning on smartphones with varying walking speed,"in Proc. of IEEE Intl. Conf. on Communications, 2016, pp. 1-6.
[11] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu,"A density-based algorithm for discovering clusters in large spatial databases with noise,"in Proc. of KDD-96, 1996, pp. 226-231.
[12] J.-G. Lee, J. Han, and K.-Y. Whang,"Trajectory clustering:A partition-and-group framework,"in Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, 2007, pp. 593-604.
[13] H. Edelsbrunner, D. G. Kirkpatrick, and R. Seidel,"On the shape of a set of points in the plane,"IEEE Trans. on Information Theory, vol. IT-29, no. 4, pp. 551-559, 1983.
[14] PCA.[Online]. Available:http://deeplearning.stanford.edu/wiki/index.php/PCA
[15] S. H. Shin, C. G. Park, J. W. Kim, et al.,"Adaptive step length estimation algorithm using low-cost MEMS inertial sensors,"in Proc. of IEEE Sensors Applications Symposium, 2007, pp. 1-5.
[16] H. Weinberg.(2002). Using the ADXL202 in pedometer and personal navigation applications.[Online]. Available:http://www.analog.com/media/en/technical-documentation/application-notes/513772624AN602.pdf
[2] D. Shin, Y. Chon, and H. Cha,"Unsupervised construction of an indoor floor plan using a smartphone,"IEEE Trans.on Systems, Man, and Cybernetics, Part C(Applications and Reviews), vol. 42, no. 6, pp. 889-898, 2012.
[3] G. Shen, Z. Chen, P. Zhang, T. Moscibroda, and Y. Zhang,"Walkie-Markie:Indoor pathway mapping made easy,"in Proc. of the 10th USENIX Conf. on Networked Systems Design and Implementation, 2013, pp. 85-98.
[4] X. Zhang, Y. Jin, H.-X. Tan, and W.-S. Soh,"CIMLoc:A crowdsourcing indoor digital map construction system for localization,"in Proc. of the IEEE 9th Intl. Conf. on Intelligent Sensors, Sensor Networks and Information Processing,2014, pp. 1-6.
[5] P. Mazumdar, V. J. Ribeiro, and S. Tewari,"Generating indoor maps by crowdsourcing positioning data from smartphones,"in Proc. of Intl. Conf. on Indoor Positioning and Indoor Navigation, 2014, pp. 322-331.
[6] B. Zhou, Q. Li, Q. Mao, W. Tu, X. Zhang, and L. Chen,"ALIMC:Activity landmark-based indoor mapping via crowdsourcing,"IEEE Trans. on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2774-2785, 2015.
[7] M. Alzantot and M. Youssef,"Crowdlnside:Automatic construction of indoor floorplans,"in Proc. of the 20th Intl.Conf. on Advances in Geographic Information Systems, 2012, pp. 99-108.
[8] Y. Jiang,Y. Xiang,X. Pan,et al.,"Hallway based automatic indoor floorplan construction using room fingerprints,"in Proc. of the ACM Intl. Joint Conf. on Pervasive and Ubiquitous Computing, 2013, pp. 315-324.
[9] D. Philipp, P. Baier, C. Dibak, et al.,"MapGENIE:Grammar-enhanced indoor map construction from crowd-sourced data,"in Proc. of IEEE Intl. Conf. on Pervasive Computing and Communications, 2014, pp. 139-147.
[10] R. Zhou,"Pedestrian dead reckoning on smartphones with varying walking speed,"in Proc. of IEEE Intl. Conf. on Communications, 2016, pp. 1-6.
[11] M. Ester, H.-P. Kriegel, J. Sander, and X. Xu,"A density-based algorithm for discovering clusters in large spatial databases with noise,"in Proc. of KDD-96, 1996, pp. 226-231.
[12] J.-G. Lee, J. Han, and K.-Y. Whang,"Trajectory clustering:A partition-and-group framework,"in Proc. of the ACM SIGMOD Intl. Conf. on Management of Data, 2007, pp. 593-604.
[13] H. Edelsbrunner, D. G. Kirkpatrick, and R. Seidel,"On the shape of a set of points in the plane,"IEEE Trans. on Information Theory, vol. IT-29, no. 4, pp. 551-559, 1983.
[14] PCA.[Online]. Available:http://deeplearning.stanford.edu/wiki/index.php/PCA
[15] S. H. Shin, C. G. Park, J. W. Kim, et al.,"Adaptive step length estimation algorithm using low-cost MEMS inertial sensors,"in Proc. of IEEE Sensors Applications Symposium, 2007, pp. 1-5.
[16] H. Weinberg.(2002). Using the ADXL202 in pedometer and personal navigation applications.[Online]. Available:http://www.analog.com/media/en/technical-documentation/application-notes/513772624AN602.pdf