{"id":141,"date":"2015-07-31T17:41:04","date_gmt":"2015-07-31T09:41:04","guid":{"rendered":"https:\/\/www.cognav.net\/?p=141"},"modified":"2015-07-31T17:45:42","modified_gmt":"2015-07-31T09:45:42","slug":"mapgenie-%e5%9f%ba%e4%ba%8e%e4%bc%97%e5%8c%85%e6%95%b0%e6%8d%ae%e7%9a%84%e5%a2%9e%e5%bc%ba%e8%af%ad%e6%b3%95%e5%ae%a4%e5%86%85%e5%9c%b0%e5%9b%be%e6%9e%84%e5%bb%ba%e6%96%b9%e6%b3%95-2","status":"publish","type":"post","link":"https:\/\/braininspirednavigation.com\/?p=141","title":{"rendered":"MapGENIE: \u57fa\u4e8e\u4f17\u5305\u6570\u636e\u7684\u589e\u5f3a\u8bed\u6cd5\u5ba4\u5185\u5730\u56fe\u6784\u5efa\u65b9\u6cd5"},"content":{"rendered":"

\n\tMapGENIE<\/span>: \u57fa\u4e8e\u4f17\u5305\u6570\u636e\u7684\u589e\u5f3a\u8bed\u6cd5\u5ba4\u5185\u5730\u56fe\u6784\u5efa\u65b9\u6cd5<\/strong><\/span><\/span>\n<\/p>\n

\n\tMapGENIE\uff1aGrammar-enhanced Indoor Map Construction from Crowd-sourced Data<\/span><\/span><\/span>\n<\/p>\n

\n\t \n<\/p>\n

\n\t        \u4f4d\u7f6e\u670d\u52a1\u5728\u5ba4\u5916\u73af\u5883\u4e2d\u5df2\u5f97\u5230\u4e86\u5e7f\u6cdb\u5e94\u7528\uff0c\u4f46\u5728\u5ba4\u5185\u73af\u5883\u4e2d\u8fd8\u4e0d\u53ef\u7528\u3002\u4e3b\u8981\u6709\u4e24\u4e2a\u65b9\u9762\u7684\u539f\u56e0\uff1a\u7b2c\u4e00\uff0c\u6ca1\u6709\u652f\u6301\u79fb\u52a8\u8bbe\u5907\u7684\u73b0\u6210\u7684\u5ba4\u5185\u5b9a\u4f4d\u7cfb\u7edf\uff1b\u7b2c\u4e8c\uff0c\u5728\u5927\u90e8\u5206\u5efa\u7b51\u4e2d\u7f3a\u4e4f\u9762\u5411\u516c\u4f17\u7684\u5ba4\u5185\u5730\u56fe\u6570\u636e\u3002\u540c\u65f6\uff0c\u5b58\u5728\u5927\u91cf\u7684\u52b3\u529b\u6210\u672c\uff0c\u4f7f\u5f97\u6709\u6548\u7684\u521b\u5efa\u5ba4\u5185\u5730\u56fe\u4ecd\u7136\u9762\u4e34\u8bb8\u591a\u6311\u6218\u3002<\/span><\/span><\/span>\n<\/p>\n

\n\t        <\/span>\u4f5c\u8005\u63d0\u51fa\u7684MapGENIE\u65b9\u6cd5\u89e3\u51b3\u4e86\u5ba4\u5185\u5236\u56fe\u7684\u95ee\u9898\uff0c\u5373\u901a\u8fc7\u641c\u96c6\u5ba4\u5185\u5efa\u7b51\u73af\u5883\u4e2d\u884c\u4eba\u7684\u8fd0\u52a8\u8f68\u8ff9\u81ea\u52a8\u7684\u6784\u5efa\u5ba4\u5185\u5730\u56fe\u3002\u7531\u4e8e\u901a\u8fc7\u884c\u4eba\u79fb\u52a8\u8bbe\u5907\u4ece\u540e\u53f0\u641c\u96c6\u8f68\u8ff9\u6570\u636e\uff0c\u907f\u514d\u4e86\u4f20\u7edf\u5ba4\u5185\u5236\u56fe\u8fc7\u7a0b\u4e2d\u82b1\u8d39\u5927\u91cf\u7684\u52b3\u529b\u6210\u672c\uff0c\u540c\u65f6\u80fd\u591f\u63d0\u9ad8\u5ba4\u5185\u5730\u56fe\u7684\u6709\u6548\u6027\u3002\u4e3a\u4e86\u6539\u8fdb\u5730\u56fe\u6784\u5efa\u8fc7\u7a0b\uff0cMapGENIE\u5229\u7528\u5efa\u7b51\u7269\u5185\u90e8\u4fe1\u606f\u5e76\u901a\u8fc7\u8bed\u6cd5\u7f16\u7801\u5efa\u7b51\u7ed3\u6784\u4fe1\u606f\u3002\u8be5\u65b9\u6cd5\u5728\u5ba4\u5185\u5236\u56fe\u8fc7\u7a0b\u4e2d\u4ec5\u9700\u8981\u5c0f\u91cf\u7684\u8f68\u8ff9\u6570\u636e\u800c\u4e0d\u9700\u8981\u7528\u6237\u7684\u53c2\u4e0e\u3002<\/span><\/span><\/span>\n<\/p>\n

\n\t        <\/span>\u4e3a\u4e86\u6d4b\u8bd5MapGENIE\u7684\u6027\u80fd\uff0c\u4f5c\u8005\u5b9e\u73b0\u4e86\u4e00\u5957\u57fa\u4e8eAndroid\u7684\u7cfb\u7edf\uff0c\u901a\u8fc7\u5fd7\u613f\u8005\u7528\u7ed1\u5728\u811a\u4e0a\u7684IMU\u641c\u96c6\u8f68\u8ff9\u6570\u636e\u3002\u4e0e\u5355\u7eaf\u7684\u57fa\u4e8e\u8f68\u8ff9\u7684\u65b9\u6cd5\u76f8\u6bd4\uff0c\u5229\u7528\u8bed\u6cd5\u7684\u65b9\u6cd5\u80fd\u591f\u8bc6\u522b\u51fa\u5efa\u7b51\u7269\u4e2d\u56db\u500d\u7684\u623f\u95f4\u6570\uff0c\u540c\u65f6\u5728\u68c0\u6d4b\u5230\u623f\u95f4\u7684\u5927\u5c0f\u65f6\u9519\u8bef\u7387\u4e5f\u66f4\u4f4e\u3002<\/span><\/span><\/span>\n<\/p>\n

\n\t \n<\/p>\n

\n\t      Abstract-While location-based services are already well established in outdoor scenarios, they are still not available in indoor environments. The reason for this can be found in two open problems: First, there is still no off-the-shelf indoor positioning system for mobile devices and, second, indoor maps are not publicly available for most buildings. While there is an extensive body of work on the first problem, the efficient creation of indoor maps remains an open challenge.<\/span><\/span><\/span>\n<\/p>\n

\n\t      <\/span>We tackle the indoor mapping challenge in our MapGENIE approach that automatically erives indoor maps from traces collected by pedestrians moving around in a building. Since the trace data is collected in the background from the pedestrians' mobile devices, MapGENIE avoids the labor-intensive task of traditional indoor map creation and increases the efficiency of indoor mapping. To enhance the map building process, MapGENIE leverages exterior information about the building and uses grammars to encode structural information about the building. Hence, in contrast to existing work, our approach works without any user interaction and only needs a small amount of traces to derive the indoor map of a building.<\/span><\/span><\/span>\n<\/p>\n

\n\t      <\/span>To demonstrate the performance of MapGENIE, we implemented our system using Android and a foot-mounted IMU to collect traces from volunteers. We show that using our grammar approach, compared to a purely trace-based approach we can identify up to four times as many rooms in a building while at the same time achieving a consistently lower error in the size of detected rooms.<\/span><\/span><\/span>\n<\/p>\n

\n\t\"\"\n<\/p>\n

\n\tSystem architecture showing the backend system and its inputs to the individual components.<\/span><\/span><\/span>\n<\/p>\n

\n\t \n<\/p>\n

\n\t\"\"\n<\/p>\n

\n\tOverview of the Trace-based Modeling component<\/span><\/span><\/span>\n<\/p>\n

\n\t\u76f8\u5173\u5de5\u4f5c<\/strong><\/span><\/span><\/span>\n<\/p>\n

\n\t       \u5927\u591a\u6570\u5ba4\u5185\u5236\u56fe\u65b9\u6cd5\u57fa\u4e8e\u4e0d\u540c\u7684SLAM\u539f\u7406\uff0cSLAM\u6280\u672f\u6e90\u81ea\u673a\u5668\u4eba\u9886\u57df\u3002\u8fd1\u5e74\u6765\uff0c\u7531\u4eba\u5458\u8fdb\u884c\u64cd\u4f5c\u5fae\u578b\u6fc0\u5149\u626b\u63cf\u4eea\u301013\u3011\u6216\u5fae\u8f6fKinect\u301014\u3011\u7684SLAM\u65b9\u6cd5\u9010\u6e10\u5174\u8d77\u3002\u4e3a\u4e86\u66ff\u4ee3\u9700\u8981\u4eba\u5de5\u53c2\u4e0e\u7684\u626b\u63cf\u6280\u672f\uff0c\u51fa\u73b0\u4e86\u5229\u7528\u5b9a\u4f4d\u8f68\u8ff9\u8fdb\u884c\u5236\u56fe\u7684\u65b9\u6cd5\uff0c\u5176\u4e2d\u9700\u8981\u7528\u5230\u4f4e\u6210\u672c\u6cdb\u5728\u53ef\u7528\u7684\u786c\u4ef6\u3002\u7cfb\u7edf\u4ec5\u5229\u7528\u8f68\u8ff9\u6570\u636e\u6765\u6784\u5efa\u5ba4\u5185\u5730\u56fe\uff0c<\/span><\/span><\/span>\n<\/p>\n

\n\t       <\/span>\u7c7b\u4f3c\u7684\u5de5\u4f5c\u6bd4\u5982 CrowdInside [2] , FootSLAM [3] \u548c SmartSLAM [15]\u3002\u5176\u4e2dFootSLAM \u5229\u7528\u7ed1\u5728\u811a\u4e0a\u7684IMU\u4f20\u611f\u5668\u6765\u6784\u5efa\u53ef\u884c\u8d70\u533a\u57df\u7684\u5ba4\u5185\u5730\u56fe\uff0c\u4e0d\u533a\u5206\u8d70\u5eca\u6216\u8005\u623f\u95f4\u3002\u4e3a\u4e86\u4e0d\u53d7\u590d\u6742\u7684\u5916\u90e8IMU\u7684\u7ea6\u675f\uff0cCrowdInside and SmartSLAM\u5229\u7528\u667a\u80fd\u624b\u673a\u5185\u7f6e\u7684\u4f20\u611f\u5668\u83b7\u53d6\u7528\u6237\u8f68\u8ff9\u6570\u636e\u3002\u7136\u800c\uff0cSmartSLAM\u53ea\u4ece\u7ed3\u679c\u6570\u636e\u96c6\u4e2d\u6784\u5efa\u8d70\u5eca\u7ed3\u6784\u3002\u800cCrowdInside\u4e00\u65b9\u9762\u9700\u8981\u6cbf\u7740\u623f\u95f4\u5899\u58c1\u884c\u8d70\u7684\u5927\u91cf\u8f68\u8ff9\u6570\u636e\u4ee5\u4fbf\u5f97\u5230\u8f83\u597d\u7684\u7ed3\u679c\uff0c\u53e6\u4e00\u65b9\u9762\uff0c\u5c06\u623f\u95f4\u6784\u5efa\u6210\u4e00\u79cdalpha\u5f62\u72b6\uff08alpha shapes\uff09\uff0c\u800c\u6ca1\u6709\u8003\u8651\u5ba4\u5185\u5efa\u7b51\u5e38\u8bc6\uff0c\u5982\u5e73\u884c\u3001\u77e9\u5f62\u3001\u91cd\u590d\u7b49\u7279\u5f81\u3002<\/span><\/span><\/span>\n<\/p>\n

\n\t       <\/span>\u6587\u732e\u301016\u3011\u4e2d\u63d0\u51fa\u7684\u65b9\u6cd5\u7ed3\u5408\u624b\u673a\u4f20\u611f\u5668\u548cWi-Fi\u6307\u7eb9\u6765\u5b66\u4e60\u8d70\u5eca\u5e03\u5c40\uff0c\u4ee5\u53ca\u533a\u5206\u4e0d\u540c\u7684\u77e9\u5f62\u623f\u95f4\u3002\u4f46\u5bf9\u4e8e\u7cbe\u786e\u91cd\u5efa\u5ba4\u5185\u7ed3\u6784\uff08\u6bd4\u5982\u91cd\u590d\u7ed3\u6784\uff09\u7684\u95ee\u9898\u8be5\u65b9\u6cd5\u4e5f\u6ca1\u6709\u5f88\u597d\u7684\u89e3\u51b3\u3002\u5728\u4eba\u9020\u5efa\u7b51\u4e2d\uff0c\u7531\u4e8e\u5e73\u884c\u548c\u77e9\u5f62\u662f\u6700\u4e3b\u8981\u7684\u89c4\u5219\uff0c\u9700\u8981\u91cd\u5efa\u65b9\u6cd5\u90fd\u662f\u57fa\u4e8eManhattan World \u7ea6\u675f\u301017\u3011\u3002\u53e6\u5916\u8fd8\u5305\u62ec\u91cd\u590d\u6027\u6216\u8005\u5efa\u6a21\u66f4\u591a\u4e00\u822c\u7ea6\u675f\u3002\u5176\u4e2d\u5f62\u5f0f\u5316\u8bed\u6cd5\uff08formal grammars\uff09\u5728\u591a\u5e74\u524d\u5df2\u7ecf\u6210\u529f\u5e94\u7528\u5728\u4e86\u5efa\u6a21\u51e0\u4f55\u7ed3\u6784\u65b9\u9762\u3002\u4f46\u6587\u732e\u301018\u3011\u4ec5\u96c6\u4e2d\u5728\u7ebf\u6027\u7ed3\u6784\uff0c\u6bd4\u5982\u901a\u8fc7 Lindenmayer-systems (Lsystems)\u4eff\u771f\u690d\u7269\u7684\u751f\u957f\u3002\u6587\u732e\u30105\u3011\u548c\u30106\u3011\u9a8c\u8bc1\u4e86\u8bed\u6cd5\u5728\u91cd\u5efa\u8857\u9053\u8def\u7f51\u548c\u5efa\u7b51\u5916\u58f3\u65b9\u9762\u7684\u6709\u7528\u6027\u3002\u5728\u5efa\u6a21\u5ba4\u5185\u5efa\u7b51\u65b9\u9762\uff0c\u6587\u732e\u301019\u3011\u63d0\u51fa\u4e86\u4e00\u79cd\u5408\u9002\u7684\u5206\u5272\u8bed\u6cd5\uff0c\u4f46\u6ca1\u6cd5\u5e94\u7528\u5728\u4ece\u4e0d\u51c6\u786e\u7684\u89c2\u6d4b\u6570\u636e\u8fdb\u884c\u91cd\u5efa\u65b9\u9762\u3002<\/span><\/span><\/span>\n<\/p>\n

\n\t \n<\/p>\n

\n\tRELATED WORK<\/span><\/span><\/span>\n<\/p>\n

\n\tMost approaches for indoor mapping build on different flavours of the Simultaneous Localization and Mapping (SLAM) principle, originating from robotics. Recently, also human-operated SLAM approaches have emerged which employ miniature laser scanners [13] , or the Microsoft Kinect [14]. Instead of using scanning techniques that require manual effort, the idea of using position traces, which may be acquired using low-cost ubiquitously available hardware, was proposed. Systems employing this data as the sole base for the reconstruction of building interiors are described in works like CrowdInside [2] , FootSLAM [3] and SmartSLAM [15]. FootSLAM-as in our approach, using a foot-mounted IMU reconstructs the building’s interior as a map of walkable areas, not distinguishing between hallways or rooms. Instead of being constrained to a dedicated external IMU, CrowdInside and SmartSLAM employ the set of sensors found in modern smartphones. However, SmartSLAM merely reconstructs hallway structures from the resulting data. CrowdInside, on the one hand, requires a large amount of traces following the room walls in order to function well and, on the other hand, reconstructs the rooms as alpha shapes, not taking knowledge about common features of interior architecture like parallelism, rectangularity, or repetition into account. Following the evaluation, CrowdInside needs 290 trace segments to fully reconstruct all corridor areas and the 12 rooms in the floor plan used in the testbed. The system presented in [16] uses a combination of smartphone sensors and WiFi fingerprints to learn hallway layouts as well as to distinguish different rectangular rooms. The authors state that the system needs only 20 data points per floor plan to converge, while delivering an average room position accuracy of 91 %, but a room area estimation error of 33% and a room aspect ratio error of 24%. The exact reconstruction of, e.g., repetitive structures is also not tackled by this approach. Due to parallelism and rectangularity being the most prominent rules used in man-made construction, many reconstruction approaches build on the Manhattan World constraints [17]. To further include, e.g., repetition or to model more general constraints, formal grammars have been applied successfully to the modeling of geometric structures for several years. While [18] focuses on line structures by, e.g., simulating growth processes of plants through Lindenmayer-systems (Lsystems), [5] and [6] proved the usability of grammars for the reconstruction of street networks and building shells. In terms of procedural modeling of building interiors, the authors of [19] present an appropriate split grammar, however, without the possibility of its use in the reconstruction from erroneous observation data.<\/span><\/span><\/span>\n<\/p>\n

\n\t \n<\/p>\n

\n\tPhilipp D, Baier P, Dibak C, et al. Mapgenie: Grammar-Enhanced Indoor Map Construction From Crowd-Sourced Data[C]\/\/ Pervasive Computing and Communications (PerCom), 2014 IEEE International Conference on2014:139 – 147.<\/span><\/span><\/span>\n<\/p>\n

\n\t \n<\/p>\n

\n\tReferences<\/span><\/span><\/span>\n<\/p>\n

\n\t[2] A. Moustafa and Y. Moustafa, "Crowdinside: Automatic construction of indoor floorplans," in Proc. Con! Advances in Geographic Information Systems (SIGSPATIAL), 2012.<\/span><\/span><\/span>\n<\/p>\n

\n\t[3] M. Angermann and P. Robertson, "Footslam: Pedestrian simultaneous localization and mapping without exteroceptive sensors – hitchhiking on human perception and cognition," Proc. of the IEEE, vol. 1 00, pp.1 840-1 848, 2012.<\/span><\/span><\/span>\n<\/p>\n

\n\t[5] P. Miiller, P. Wouka, S. Haegler, A. Ulmer, and L. Van Gool, "Procedural modeling of buildings;' ACM Trans. Graph. , vol. 25, pp. 6 1 4–623, 2006.<\/span><\/span><\/span>\n<\/p>\n

\n\t[6] Y. I. H. Parish and P. Miiller, "Procedural modeling of cities," in Proc. 28th Annu. Con! Compo Graph. and Interactive Techniques, ser. SIGGRAPH ' 0 1 . New York, NY, USA: ACM, 2001 , pp. 301-308.<\/span><\/span><\/span>\n<\/p>\n

\n\t[13] M. Bosse, R. Zlot, and P. Flick, "Zebedee: Design o f a spring-mounted 3-d range sensor with application to mobile mapping," IEEE Trans. Robotics, vol. 28, pp. 1 1 04–1 1 19, 2012.<\/span><\/span><\/span>\n<\/p>\n

\n\t[14] R. A. Newcombe, A. J. Davison, S. Izadi, P. Kohli, O. Hilliges, J. Shotton, D. Molyneaux, S. Hodges, D. Kim, and A. Fitzgibbon, "KinectFusion: real-time dense surface mapping and tracking," in Proc. Symp. Mixed and Augmented Reality (ISMAR), 2011 .<\/span><\/span><\/span>\n<\/p>\n

\n\t[15] H. Shin, Y. Chon, and H. Cha, "Unsupervised construction of an indoor floor plan using a smartphone," IEEE Trans. Syst., Man, and Cybernetics, C: Applicat. and Reviews, vol. 42, pp. 889-89 8, 2012.<\/span><\/span><\/span>\n<\/p>\n

\n\t[16] Y. Jiang, Y. Xiang, X. Pan, K. Li, Q. Lv, R. P. Dick, L. Shang, and M. Hannigan, "Hallway based automatic indoor floorplan construction using room fingerprints;' in Proc. Joint Con! Pervasive and Ubiquitous Computing ( UbiComp), 2013.<\/span><\/span><\/span>\n<\/p>\n

\n\t[17] J. M. Coughlan and A. L. Yuille, "Manhattan world: Compass direction from a single image by bayesian inference;' in Proc. 7th IEEE Int. Con! Compo Vision, vol. 2, 1999, pp. 941-947.<\/span><\/span><\/span>\n<\/p>\n

\n\t[18] P. Prusinkiewicz and A. Lindenmayer, The algorithmic beauty of plants. Springer New York, 1 990.<\/span><\/span><\/span>\n<\/p>\n

\n\t[19] G. Groger and L. Pliimer, "Derivation of 3D indoor models by grammars for route planning," Photogrammetrie-Fernerkundung-Geoinformation, vol. 2010, pp. 1 93-210, 2010.<\/span><\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

MapGENIE: \u57fa\u4e8e\u4f17\u5305\u6570\u636e\u7684\u589e\u5f3a\u8bed\u6cd5\u5ba4\u5185\u5730\u56fe\u6784\u5efa\u65b9\u6cd5 MapGENIE\uff1aGrammar-enhanced Indoor Map Construction from Crowd-sourced Data           \u4f4d\u7f6e\u670d\u52a1\u5728\u5ba4\u5916\u73af\u5883\u4e2d\u5df2\u5f97\u5230\u4e86\u5e7f\u6cdb\u5e94\u7528\uff0c\u4f46\u5728\u5ba4\u5185\u73af\u5883\u4e2d\u8fd8\u4e0d\u53ef\u7528\u3002\u4e3b\u8981\u6709\u4e24\u4e2a\u65b9\u9762\u7684\u539f\u56e0\uff1a\u7b2c\u4e00\uff0c\u6ca1\u6709\u652f\u6301\u79fb\u52a8\u8bbe\u5907\u7684\u73b0\u6210\u7684\u5ba4\u5185\u5b9a\u4f4d\u7cfb\u7edf\uff1b\u7b2c\u4e8c\uff0c\u5728\u5927\u90e8\u5206\u5efa\u7b51\u4e2d\u7f3a\u4e4f\u9762\u5411\u516c\u4f17\u7684\u5ba4\u5185\u5730\u56fe\u6570\u636e\u3002\u540c\u65f6\uff0c\u5b58\u5728\u5927\u91cf\u7684\u52b3\u529b\u6210\u672c\uff0c\u4f7f\u5f97\u6709\u6548\u7684\u521b\u5efa\u5ba4\u5185\u5730\u56fe\u4ecd\u7136\u9762\u4e34\u8bb8\u591a\u6311\u6218\u3002         \u4f5c\u8005\u63d0\u51fa\u7684MapGENIE\u65b9\u6cd5\u89e3\u51b3\u4e86\u5ba4\u5185\u5236\u56fe\u7684\u95ee\u9898\uff0c\u5373\u901a\u8fc7\u641c\u96c6\u5ba4\u5185\u5efa\u7b51\u73af\u5883\u4e2d\u884c\u4eba\u7684\u8fd0\u52a8\u8f68\u8ff9\u81ea\u52a8\u7684\u6784\u5efa\u5ba4\u5185\u5730\u56fe\u3002\u7531\u4e8e\u901a\u8fc7\u884c\u4eba\u79fb\u52a8\u8bbe\u5907\u4ece\u540e\u53f0\u641c\u96c6\u8f68\u8ff9\u6570\u636e\uff0c\u907f\u514d\u4e86\u4f20\u7edf\u5ba4\u5185\u5236\u56fe\u8fc7\u7a0b\u4e2d\u82b1\u8d39\u5927\u91cf\u7684\u52b3\u529b\u6210\u672c\uff0c\u540c\u65f6\u80fd\u591f\u63d0\u9ad8\u5ba4\u5185\u5730\u56fe\u7684\u6709\u6548\u6027\u3002\u4e3a\u4e86\u6539\u8fdb\u5730\u56fe\u6784\u5efa\u8fc7\u7a0b\uff0cMapGENIE\u5229\u7528\u5efa\u7b51\u7269\u5185\u90e8\u4fe1\u606f\u5e76\u901a\u8fc7\u8bed\u6cd5\u7f16\u7801\u5efa\u7b51\u7ed3\u6784\u4fe1\u606f\u3002\u8be5\u65b9\u6cd5\u5728\u5ba4\u5185\u5236\u56fe\u8fc7\u7a0b\u4e2d\u4ec5\u9700\u8981\u5c0f\u91cf\u7684\u8f68\u8ff9\u6570\u636e\u800c\u4e0d\u9700\u8981\u7528\u6237\u7684\u53c2\u4e0e\u3002         \u4e3a\u4e86\u6d4b\u8bd5MapGENIE\u7684\u6027\u80fd\uff0c\u4f5c\u8005\u5b9e\u73b0\u4e86\u4e00\u5957\u57fa\u4e8eAndroid\u7684\u7cfb\u7edf\uff0c\u901a\u8fc7\u5fd7\u613f\u8005\u7528\u7ed1\u5728\u811a\u4e0a\u7684IMU\u641c\u96c6\u8f68\u8ff9\u6570\u636e\u3002\u4e0e\u5355\u7eaf\u7684\u57fa\u4e8e\u8f68\u8ff9\u7684\u65b9\u6cd5\u76f8\u6bd4\uff0c\u5229\u7528\u8bed\u6cd5\u7684\u65b9\u6cd5\u80fd\u591f\u8bc6\u522b\u51fa\u5efa\u7b51\u7269\u4e2d\u56db\u500d\u7684\u623f\u95f4\u6570\uff0c\u540c\u65f6\u5728\u68c0\u6d4b\u5230\u623f\u95f4\u7684\u5927\u5c0f\u65f6\u9519\u8bef\u7387\u4e5f\u66f4\u4f4e\u3002         Abstract-While location-based services are already well established in outdoor scenarios, they are still not available in indoor environments. The reason for this can be found in two open […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[9],"tags":[],"_links":{"self":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/141"}],"collection":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=141"}],"version-history":[{"count":2,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/141\/revisions"}],"predecessor-version":[{"id":143,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=\/wp\/v2\/posts\/141\/revisions\/143"}],"wp:attachment":[{"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=141"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=141"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/braininspirednavigation.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=141"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}