随著智慧型手機技術的快速發展及其導航傳感器的改進，現在在可獲得越來越多的位置資訊，這為提供新的智能交通系统（Intelligent transportation system, ITS）服務打開了大門。 現代智慧型手機包含嵌入式全球衛星導航系统（Global navigation satellite systems, GNSSs），慣性測量元件（Inertial measurement unit, IMU），这些慣性測量元件具有三軸加速度計和陀螺儀傳感器，磁力計，照相機以及其他能夠提供用戶位置，速度和姿態的傳感器。 但是，由於制造商採用的軟體技術以及環境條件的影響，手機傳感器普遍品質較低且彼此間存在較大差異，使得難以利用智慧型手機實現良好的導航性能。
在這項研究中，我們提出了多傳感器融合方案，該方案使用傳統的估計算法-擴增式卡漫濾波器（Extended Kalman filter, EKF）和自適應卡漫濾波器（Adaptive Kalman filter, AKF）集合了來自智慧型手機的傳感器資料，以增强車輛和行人的導航性能。使用定向的FAST（Features from accelerated segment test, 來自加速段測試的特徵）和旋轉的Brief（Binary robust independent elementary features, 二進制自動化獨立基本特徵）-同時進行的定位和映射（Simultaneous localization and mapping, ORB-SLAM）預處理來自相機的影像序列，通過應用GNSS測量進行重新縮放，然後轉换為速度資訊被用來更新融合系统。 IMU資訊是根據導航模式以不同的策略進行處理的。在車輛導航應用中，慣性導航系统（Inertial navigation system, INS）用于建立系统模型以與其他觀測量融合。對於行人導航應用，在融合視覺策略之前，使用行人航位推算（Pedestrian dead reckoning, PDR）算法處理IMU和磁力計資訊。
為了驗證融合系统的性能，在室内環境以及台灣的台南市區和台北市收集了各種現場測試資料。 實驗结果表明，視覺資訊對於提高導航性能的準確性有極大的幫助。 使用融合智慧型手機傳感器資訊的多感測器融合策略對於嚴峻的GNSS環境中提高導航精度和自動化有方面是有效的。

With the rapid growth in smartphone technologies and improvement in their navigation sensors, an increasing amount of location information is now available, opening the door to the provision of new intelligent transportation system (ITS) services. Modern smartphones contain embedded global navigation satellite systems (GNSSs), inertial measurement unit (IMU) that featured three-axial accelerometer and gyroscope sensors, magnetometers, cameras, and other sensors which are capable of providing user position, velocity, and attitude. However, it is difficult to utilize the actual navigation performance capabilities of smartphones due to low quality and disparate sensors, software technologies adopted by manufacturers, and the significant influence of environmental conditions.
In this study, we proposed multi-fusion schemes that integrated sensors’ data from smartphone using conventional estimation algorithm - extended Kalman filter (EKF), and adaptive Kalman filter (AKF) to enhance the navigation performance of both vehicle and pedestrian. The image sequence from camera was preprocessed using oriented FAST (Features from accelerated segment test) and rotated BRIEF (Binary robust independent elementary features)-simultaneous localization and mapping (ORB-SLAM), rescaled by applying GNSS measurements, and converted to velocity data before being utilized to update the integration system. The IMU data was processed in different strategies depending on navigation mode. In case of vehicular navigation applications, inertial navigation system (INS) mechanization was used to build a system model to fuse with the other measurements. For pedestrian navigation applications, pedestrian dead reckoning (PDR) algorithm was used to process IMU and magnetometer data before fusing with visual solution.
In order to verify the performance of the integrated system, various field tests data were collected in indoor environments, and in downtown areas of Tainan and Taipei Cities, Taiwan. Experimental results indicated that visual data significantly contributed to improve the accuracy of the navigation performance. It was verified that the multi-fusion schemes, which used data from smartphone sensors, were efficient in terms of increasing navigation accuracy and robustness in GNSS-challenging environments.

Afia, A.B., 2017. Development of GNSS/INS/SLAM Algorithms for Navigation in Constrained Environments.
Aggarwal, P., Syed, Z., El-Sheimy, N., 2009. Hybrid extended particle filter (HEPF) for integrated inertial navigation and global positioning systems. Measurement Science and Technology 20, 055203.
Aggarwal, P., Syed, Z., Niu, X., El-Sheimy, N., 2008. A standard testing and calibration procedure for low cost MEMS inertial sensors and units. The Journal of Navigation 61, 323.
Agrawal, M., Konolige, K., Blas, M.R., 2008. Censure: Center surround extremas for realtime feature detection and matching, European Conference on Computer Vision. Springer, pp. 102-115.
Aguilar-Gonzalez, A., 2019. Monocular-SLAM dense mapping algorithm and hardware architecture for FPGA acceleration. Université Clermont Auvergne; Instituto Nacional de Astrofisica, Optica y ….
Ahmed, D.B., Díez, L.E., Diaz, E.M., Domínguez, J.J.G., 2019. A Survey on Test and Evaluation Methodologies of Pedestrian Localization Systems. IEEE Sensors Journal 20, 479-491.
Al-Hamad, A., El-Sheimy, N., 2014. Smartphones based mobile mapping systems. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40, 29.
Ali, A., 2013. Low-cost sensors-based attitude estimation for pedestrian navigation in GPS-denied environments.
Almagbile, A., Wang, J., Ding, W., 2010. Evaluating the performances of adaptive Kalman filter methods in GPS/INS integration. Journal of Global Positioning Systems 9, 33-40.
Aqel, M.O., Marhaban, M.H., Saripan, M.I., Ismail, N.B., 2016. Review of visual odometry: types, approaches, challenges, and applications. SpringerPlus 5, 1897.
Artal, R.M., 2017. Real-Time Accurate Visual SLAM with Place Recognition. Universidad de Zaragoza.
Bailey, T., Durrant-Whyte, H., 2006. Simultaneous localization and mapping (SLAM): Part II. IEEE robotics & automation magazine 13, 108-117.
Bay, H., Tuytelaars, T., Van Gool, L., 2006. Surf: Speeded up robust features, European conference on computer vision. Springer, pp. 404-417.
Bishop, E., Li, Q., 2010. Walking speed estimation using shank-mounted accelerometers, 2010 IEEE International Conference on Robotics and Automation. IEEE, pp. 5096-5101.
Bloesch, M., Omari, S., Hutter, M., Siegwart, R., 2015. Robust visual inertial odometry using a direct EKF-based approach, 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp. 298-304.
Bousdar Ahmed, D., Munoz Diaz, E., García Domínguez, J.J., 2020. Novel Multi-IMU Tight Coupling Pedestrian Localization Exploiting Biomechanical Motion Constraints. Sensors 20, 5364.
Bradski, G., Kaehler, A., 2000. OpenCV. Dr. Dobb’s journal of software tools 3.
Britting, K.R., 1971. Inertial navigation systems analysis.
Brown, R.G., Hwang, P.Y., 1992. Introduction to random signals and applied Kalman filtering, second edition ed. Wiley New York.
Brown, R.G., Hwang, P.Y., 1997. Introduction to random signals and applied Kalman filtering: with MATLAB Exercises and Solutions, third edition ed. John Wiley & Sons New York, NY, USA.
Cadena, C., Carlone, L., Carrillo, H., Latif, Y., Scaramuzza, D., Neira, J., Reid, I., Leonard, J.J., 2016. Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age. IEEE Transactions on robotics 32, 1309-1332.
Calonder, M., Lepetit, V., Strecha, C., Fua, P., 2010. Brief: Binary robust independent elementary features, European conference on computer vision. Springer, pp. 778-792.
Campos, C., Elvira, R., Rodríguez, J.J.G., Montiel, J.M., Tardós, J.D., 2020. ORB-SLAM3: An accurate open-source library for visual, visual-inertial and multi-map SLAM. arXiv preprint arXiv:2007.11898.
Chang, H.-W., 2014. Enhanced Portable Navigation for Cycling Applications.
Chen, Y.-L., 2018. The Performance Analysis of an AKF-based INS/GNSS Integration Scheme with Vehicular and Vertical Constraints in Urban Environment. MSc Thesis, Department of Geomatics, National Cheng Kung University, Tainan, Taiwan.
Chiang, K.-W., Chang, H.-W., 2010. Intelligent sensor positioning and orientation through constructive neural network-embedded INS/GPS integration algorithms. Sensors 10, 9252-9285.
Chiang, K.-W., Chang, H.-W., Li, C.-Y., Huang, Y.-W., 2009. An artificial neural network embedded position and orientation determination algorithm for low cost MEMS INS/GPS integrated sensors. Sensors 9, 2586-2610.
Chiang, K.-W., Duong, T.T., Liao, J.-K., 2013. The performance analysis of a real-time integrated INS/GPS vehicle navigation system with abnormal GPS measurement elimination. Sensors 13, 10599-10622.
Chiang, K.-W., Duong, T.T., Liao, J.-K., Lai, Y.-C., Chang, C.-C., Cai, J.-M., Huang, S.-C., 2012. On-line smoothing for an integrated navigation system with low-cost MEMS inertial sensors. Sensors 12, 17372-17389.
Chiang, K.-W., Huang, Y.-W., Li, C.-Y., Chang, H.-W., 2011. An ANN embedded RTS smoother for an INS/GPS integrated positioning and orientation system. Applied Soft Computing 11, 2633-2644.
Chiang, K.-W., Le, D.T., Duong, T.T., Sun, R., 2020a. The Performance Analysis of INS/GNSS/V-SLAM Integration Scheme Using Smartphone Sensors for Land Vehicle Navigation Applications in GNSS-Challenging Environments. Remote Sensing 12, 1732.
Chiang, K.-W., Liao, J.-K., Tsai, G.-J., Chang, H.-W., 2016. The performance analysis of the map-aided fuzzy decision tree based on the pedestrian dead reckoning algorithm in an indoor environment. Sensors 16, 34.
Chiang, K.-W., Tsai, G.-J., Chu, H.-J., El-Sheimy, N., 2020b. Performance enhancement of ins/gnss/refreshed-slam integration for acceptable lane-level navigation accuracy. IEEE Transactions on Vehicular Technology 69, 2463-2476.
Davidson, P., Piché, R., 2016. A survey of selected indoor positioning methods for smartphones. IEEE Communications Surveys & Tutorials 19, 1347-1370.
Davison, A.J., 2003. Real-time simultaneous localisation and mapping with a single camera, Proc. Int. Conf. Computer Vision. IEEE, p. 1403.
Davison, A.J., Reid, I.D., Molton, N.D., Stasse, O., 2007. MonoSLAM: Real-time single camera SLAM. IEEE transactions on pattern analysis and machine intelligence 29, 1052-1067.
Dimirovski, G.M., 2016. Complex Systems: Relationships Between Control, Communications and Computing. Springer.
Dissanayake, G., Sukkarieh, S., Nebot, E., Durrant-Whyte, H., 2001. The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications. IEEE transactions on robotics and automation 17, 731-747.
Duong, T.T., Chiang, K.-W., Le, D.T., 2019. On-line Smoothing and Error Modelling for Integration of GNSS and Visual Odometry. Sensors 19, 5259.
Durrant-Whyte, H., Bailey, T., 2006. Simultaneous localization and mapping: part I. IEEE robotics & automation magazine 13, 99-110.
El-Sheimy, N., 1996. The development of VISAT: a mobile survey system for GIS applications. University of Calgary.
El-Sheimy, N., 2006. Inertial techniques and INS/DGPS integration. Engo 623-Course Notes, 170-182.
Engel, J., Koltun, V., Cremers, D., 2017. Direct sparse odometry. IEEE transactions on pattern analysis and machine intelligence 40, 611-625.
Engel, J., Schöps, T., Cremers, D., 2014. LSD-SLAM: Large-scale direct monocular SLAM, European conference on computer vision. Springer, pp. 834-849.
Engel, J., Stückler, J., Cremers, D., 2015. Large-scale direct SLAM with stereo cameras, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 1935-1942.
Farrell, J., Barth, M., 1999. The global positioning system and inertial navigation. Mcgraw-hill New York, NY, USA:.
Feliz Alonso, R., Zalama Casanova, E., Gómez García-Bermejo, J., 2009. Pedestrian tracking using inertial sensors.
Fischler, M.A., Bolles, R.C., 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24, 381-395.
Forster, C., Pizzoli, M., Scaramuzza, D., 2014. SVO: Fast semi-direct monocular visual odometry, 2014 IEEE international conference on robotics and automation (ICRA). IEEE, pp. 15-22.
Forster, C., Zhang, Z., Gassner, M., Werlberger, M., Scaramuzza, D., 2016. SVO: Semidirect visual odometry for monocular and multicamera systems. IEEE Transactions on Robotics 33, 249-265.
Foxlin, E., 2005. Pedestrian tracking with shoe-mounted inertial sensors. IEEE Computer graphics and applications 25, 38-46.
Fraundorfer, F., Scaramuzza, D., 2012. Visual odometry, Part 2. IEEE Robotics & Automation Magazine 19, 78-90.
Gao, X., Wang, R., Demmel, N., Cremers, D., 2018. LDSO: Direct sparse odometry with loop closure, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 2198-2204.
Geiger, A., Moosmann, F., Car, Ö., Schuster, B., 2012. Automatic camera and range sensor calibration using a single shot, Robotics and Automation (ICRA), 2012 IEEE International Conference. IEEE, pp. 3936-3943.
Gelb, A., 1974. Applied optimal estimation. MIT press.
Gikas, V., Perakis, H., 2016. Rigorous performance evaluation of smartphone GNSS/IMU sensors for ITS applications. Sensors 16, 1240.
Gleason, S., Gebre-Egziabher, D., 2009. GNSS applications and methods.
Godha, S., 2006. Performance evaluation of low cost MEMS-based IMU integrated with GPS for land vehicle navigation application. UCGE report.
Gonzalez, R.C., Woods, R.E., Eddins, S.L., 2004. Digital image processing using MATLAB. Pearson Education India.
Grewal, M.S., Andrews, A., 2001. Kalman filtering: theory and practice using MATLAB. ISBN-10 829640672.
Grewal, M.S., Andrews, A.P., Bartone, C.G., 2020. Global navigation satellite systems, inertial navigation, and integration. John Wiley & Sons.
Groves, P.D., 2013. Principles of GNSS, Inertial, and Multisensor Integrated Navigation System. Artech House, London, UK.
Hajiyev, C., Soken, H.E., Vural, S.Y., 2015. State estimation and control for low-cost unmanned aerial vehicles. Springer.
Harris, C.G., Pike, J., 1988. 3D positional integration from image sequences. Image and Vision Computing 6, 87-90.
Hayal, A.G., 2010. Static calibration of the tactical grade inertial measurement units. The Ohio State University.
Hide, C., Moore, T., Smith, M., 2003. Adaptive Kalman filtering for low-cost INS/GPS. The Journal of Navigation 56, 143-152.
Hou, H., 2004. Modeling inertial sensors errors using Allan variance. University of Calgary, Department of Geomatics Engineering.
Jekeli, C., 2000. Inertial Navigation Systems with Geodetic Applications. Walter deGruyter. Inc., Berlin.
Jekeli, C., 2012. Geometric reference systems in geodesy (2012 edition). Division of Geodetic Science, School of Earth Sciences, Ohio State University.
Jian, Y.-D., Chen, C.-S., 2010. Two-view motion segmentation with model selection and outlier removal by ransac-enhanced dirichlet process mixture models. International Journal of Computer Vision 88, 489-501.
Jinyu, L., Bangbang, Y., Danpeng, C., Nan, W., Guofeng, Z., Hujun, B., 2019. Survey and evaluation of monocular visual-inertial SLAM algorithms for augmented reality. Virtual Reality & Intelligent Hardware 1, 386-410.
Kaplan, E., Hegarty, C., 2005. Understanding GPS: principles and applications. Artech house.
Kim, J.W., Jang, H.J., Hwang, D.-H., Park, C., 2004. A step, stride and heading determination for the pedestrian navigation system. Journal of Global Positioning Systems 3, 273-279.
Klein, G., Murray, D., 2007. Parallel tracking and mapping for small AR workspaces, Mixed and Augmented Reality, 2007. ISMAR 2007. 6th IEEE and ACM International Symposium. IEEE, pp. 225-234.
Klein, G., Murray, D., 2009. Parallel tracking and mapping on a camera phone, 2009 8th IEEE International Symposium on Mixed and Augmented Reality. IEEE, pp. 83-86.
Ladetto, Q., 2000. On foot navigation: continuous step calibration using both complementary recursive prediction and adaptive Kalman filtering, Proceedings of ION GPS, pp. 1735-1740.
Leick, A., Rapoport, L., Tatarnikov, D., 2015. GPS satellite surveying. John Wiley & Sons.
Leutenegger, S., Chli, M., Siegwart, R.Y., 2011. BRISK: Binary robust invariant scalable keypoints, 2011 International conference on computer vision. Ieee, pp. 2548-2555.
Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., Furgale, P., 2015. Keyframe-based visual–inertial odometry using nonlinear optimization. The International Journal of Robotics Research 34, 314-334.
Li, Z., Zhao, L., Wang, Y., Yang, Z., Liu, Z., Wang, C., 2020. Variance optimization of UWB observation based on map matching for PPP/INS/UWB tightly coupled positioning. Measurement Science and Technology.
Liao, J.-K., 2017. Using Smartphones for Enhanced Integrated System Strategies for Indoor Pedestrian Positioning. PhD Dissertation, Department of Geomatics, National Cheng Kung University, Tainan, Taiwan.
Lim, H., Lim, J., Kim, H.J., 2014. Real-time 6-DOF monocular visual SLAM in a large-scale environment, Robotics and Automation (ICRA), 2014 IEEE International Conference. IEEE, pp. 1532-1539.
Liu, C.-Y., Lin, C.-A., Chiang, K.-W., Huang, S.-C., Chang, C.-C., Cai, J.-M., 2012. Performance evaluation of real-time MEMS INS/GPS integration with ZUPT/ZIHR/NHC for land navigation, 2012 12th International Conference on ITS Telecommunications. IEEE, pp. 584-588.
Longuet-Higgins, H.C., 1981. A computer algorithm for reconstructing a scene from two projections. Nature 293, 133.
Lowe, D.G., 2004. Distinctive image features from scale-invariant keypoints. International journal of computer vision 60, 91-110.
Martínez-Carranza, J., Loewen, N., Márquez, F., García, E.O., Mayol-Cuevas, W., 2015. Towards autonomous flight of micro aerial vehicles using ORB-SLAM, 2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS). IEEE, pp. 241-248.
Maybeck, P.S., 1982. Stochastic models, estimation, and control. Academic press.
Maybeck, P.S., 1994. Stochastic models, estimation, and control. Volume 1. New York, Academic Press, Inc.(Mathematics in Science and Engineering 141, 438.
Mehra, R., 1971. On-line identification of linear dynamic systems with applications to Kalman filtering. IEEE Transactions on Automatic Control 16, 12-21.
Mohamed, A., Schwarz, K., 1999. Adaptive Kalman filtering for INS/GPS. Journal of geodesy 73, 193-203.
Moravec, H.P., 1980. Obstacle avoidance and navigation in the real world by a seeing robot rover. Stanford Univ CA Dept of Computer Science.
Mourikis, A.I., Roumeliotis, S.I., 2007. A multi-state constraint Kalman filter for vision-aided inertial navigation, Proceedings 2007 IEEE International Conference on Robotics and Automation. IEEE, pp. 3565-3572.
Mur-Artal, R., Montiel, J.M.M., Tardos, J.D., 2015. ORB-SLAM: a versatile and accurate monocular SLAM system. IEEE Transactions on Robotics 31, 1147-1163.
Mur-Artal, R., Tardós, J.D., 2017. Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras. IEEE Transactions on Robotics 33, 1255-1262.
Nassar, S., 2003. Improving the inertial navigation system (INS) error model for INS and INS/DGPS applications. University of Calgary, Department of Geomatics Engineering.
Nassar, S., Niu, X., El-Sheimy, N., 2007. Land-vehicle INS/GPS accurate positioning during GPS signal blockage periods. Journal of Surveying Engineering 133, 134-143.
Nistér, D., 2004. An efficient solution to the five-point relative pose problem. IEEE transactions on pattern analysis and machine intelligence 26, 756-770.
Niu, X., Zhang, Q., Li, Y., Cheng, Y., Shi, C., 2012. Using inertial sensors of iPhone 4 for car navigation, Proceedings of the 2012 IEEE/ION Position, Location and Navigation Symposium. IEEE, pp. 555-561.
Novak, K., 1993. Mobile mapping systems: new tools for the fast collection of GIS information, State-of-the-Art Mapping. International Society for Optics and Photonics, pp. 188-198.
Peng, K.-Y., Lin, C.-A., Chiang, K.-W., 2012. The performance analysis of an AKF based tightly-coupled INS/GPS integrated positioning and orientation scheme with odometer and non-holonomic constraints. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci, 481-486.
Qin, T., Li, P., Shen, S., 2018. Vins-mono: A robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics 34, 1004-1020.
Rau, J.-Y., Habib, A.F., Kersting, A.P., Chiang, K.-W., Bang, K.-I., Tseng, Y.-H., Li, Y.-H., 2011. Direct sensor orientation of a land-based mobile mapping system. Sensors 11, 7243-7261.
Rauch, H.E., Tung, F., Striebel, C., 1965. Maximum likelihood estimates of linear dynamic systems. AIAA journal 3, 1445-1450.
Rogers, R.M., 2007. Applied mathematics in integrated navigation systems. American Institute of Aeronautics and Astronautics.
Rosin, P.L., Lai, Y.-K., Shao, L., Liu, Y., 2019. RGB-D Image Analysis and Processing. Springer.
Rosten, E., Drummond, T., 2006. Machine learning for high-speed corner detection, European conference on computer vision. Springer, pp. 430-443.
Rublee, E., Rabaud, V., Konolige, K., Bradski, G., 2011. ORB: An efficient alternative to SIFT or SURF, 2011 International conference on computer vision. Ieee, pp. 2564-2571.
Sabatini, A.M., Martelloni, C., Scapellato, S., Cavallo, F., 2005. Assessment of walking features from foot inertial sensing. IEEE Transactions on biomedical engineering 52, 486-494.
Sagawa, K., Inooka, H., Satoh, Y., 2000. Non-restricted measurement of walking distance, Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics.'cybernetics evolving to systems, humans, organizations, and their complex interactions'(cat. no. 0. IEEE, pp. 1847-1852.
Scaramuzza, D., Fraundorfer, F., 2011. Visual odometry, Part 1. IEEE robotics & automation magazine 18, 80-92.
Schwarz, K., Wei, M., 2000. INS/GPS integration for geodetic applications. Lecture Notes ENGO 623.
Shi, J., 1994. Good features to track, 1994 Proceedings of IEEE conference on computer vision and pattern recognition. IEEE, pp. 593-600.
Shin, E.-H., 2001. Accuarcy improvement of low cost INS/GPS for land applications. Graduate Studies.
Shin, E.-H., 2005. Estimation techniques for low-cost inertial navigation, UCGE report. PhD Dissertation, Department of Geomatics Engineering, University of Calgary, Alberta, Canada.
Spaenlehauer, A., Frémont, V., Şekercioğlu, Y.A., Fantoni, I., 2017. A loosely-coupled approach for metric scale estimation in monocular vision-inertial systems, Multisensor Fusion and Integration for Intelligent Systems (MFI), 2017 IEEE International Conference. IEEE, pp. 137-143.
Speroni, E.A., Ceolin, S.R., dos Santos, O.M., Legg, A.P., 2018. A low cost VSLAM prototype using webcams and a smartphone for outdoor application, Proceedings of the 33rd Annual ACM Symposium on Applied Computing, pp. 268-275.
Strasdat, H., Montiel, J., Davison, A.J., 2010. Real-time monocular SLAM: Why filter?, Robotics and Automation (ICRA), 2010 IEEE International Conference. IEEE, pp. 2657-2664.
Sukkarieh, S., 2000. Low cost, high integrity, aided inertial navigation systems for autonomous land vehicles.
Syed, Z.F., 2009. Design and implementation issues of a portable navigation system. PhD Dissertation, Department of Geomatics Engineering, University of Calgary, Alberta, Canada.
Teunissen, P., Montenbruck, O., 2017. Springer handbook of global navigation satellite systems. Springer.
Tien, Y., Chen, Y., Chiang, K., 2019. ADAPTIVE STRATEGY-BASED TIGHTLY-COUPLED INS/GNSS INTEGRATION SYSTEM AIDED BY ODOMETER AND BAROMETER. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.
Titterton, D., Weston, J., 2004. Strapdown inertial navigation technology. MIT Press.
Torres-Sospedra, J., Jiménez, A.R., Moreira, A., Lungenstrass, T., Lu, W.-C., Knauth, S., Mendoza-Silva, G.M., Seco, F., Pérez-Navarro, A., Nicolau, M.J., 2018. Off-line evaluation of mobile-centric indoor positioning systems: The experiences from the 2017 IPIN competition. Sensors 18, 487.
Toth, C.K., 2009. R&D of mobile LiDAR mapping and future trends, Proc. ASPRS Annu. Conf, pp. 9-13.
Von Stumberg, L., Usenko, V., Cremers, D., 2018. Direct sparse visual-inertial odometry using dynamic marginalization, 2018 IEEE International Conference on Robotics and Automation (ICRA). IEEE, pp. 2510-2517.
Wahlström, J., Skog, I., Händel, P., 2017. Smartphone-based vehicle telematics: A ten-year anniversary. IEEE Transactions on Intelligent Transportation Systems 18, 2802-2825.
Walter, O., Schmalenstroeer, J., Engler, A., Haeb-Umbach, R., 2013. Smartphone-based sensor fusion for improved vehicular navigation, 2013 10th Workshop on Positioning, Navigation and Communication (WPNC). IEEE, pp. 1-6.
Wang, J.-H., 2006. Intelligent MEMS INS/GPS integration for land vehicle navigation.
Wang, J., Stewart, M., Tsakiri, M., 2000. Adaptive Kalman filtering for integration of GPS with GLONASS and INS, Geodesy Beyond 2000. Springer, pp. 325-330.
Wang, R., Schworer, M., Cremers, D., 2017. Stereo DSO: Large-scale direct sparse visual odometry with stereo cameras, Proceedings of the IEEE International Conference on Computer Vision, pp. 3903-3911.
Weinberg, H., 2002. Using the ADXL202 in pedometer and personal navigation applications. Analog Devices AN-602 application note 2, 1-6.
Wendel, J., Trommer, G.F., 2004. Tightly coupled GPS/INS integration for missile applications. Aerospace Science and Technology 8, 627-634.
Wu, K., Ahmed, A., Georgiou, G.A., Roumeliotis, S.I., 2015. A Square Root Inverse Filter for Efficient Vision-aided Inertial Navigation on Mobile Devices, Robotics: Science and Systems.
Yan, J., He, G., Basiri, A., Hancock, C., 2019. 3D Passive-Vision-Aided Pedestrian Dead Reckoning for Indoor Positioning. IEEE Transactions on Instrumentation and Measurement.
Yang, Y., 2008. Tightly coupled MEMS INS/GPS integration with INS aided receiver tracking loops. Department of Geomatics Engineering.
Younes, G., Asmar, D., Shammas, E., Zelek, J., 2017. Keyframe-based monocular SLAM: design, survey, and future directions. Robotics and Autonomous Systems 98, 67-88.
Yousif, K., Bab-Hadiashar, A., Hoseinnezhad, R., 2015. An overview to visual odometry and visual SLAM: Applications to mobile robotics. Intelligent Industrial Systems 1, 289-311.
Zabih, R., Woodfill, J., 1994. Non-parametric local transforms for computing visual correspondence, European conference on computer vision. Springer, pp. 151-158.
Zeng, Q., Wang, J., Meng, Q., Zhang, X., Zeng, S., 2017. Seamless pedestrian navigation methodology optimized for indoor/outdoor detection. IEEE Sensors Journal 18, 363-374.
Zhang, Z., 2000. A flexible new technique for camera calibration. IEEE Transactions on pattern analysis and machine intelligence 22.