{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:13:07Z","timestamp":1760238787077,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T00:00:00Z","timestamp":1599177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we present a novel pedestrian indoor positioning system that uses sensor fusion between a foot-mounted inertial measurement unit (IMU) and a vision-based fiducial marker tracking system. The goal is to provide an after-action review for first responders during training exercises. The main contribution of this work comes from the observation that different walking types (e.g., forward walking, sideways walking, backward walking) lead to different levels of position and heading error. Our approach takes this into account when accumulating the error, thereby leading to more-accurate estimations. Through experimentation, we show the variation in error accumulation and the improvement in accuracy alter when and how often to activate the camera tracking system, leading to better balance between accuracy and power consumption overall. The IMU and vision-based systems are loosely coupled using an extended Kalman filter (EKF) to ensure accurate and unobstructed positioning computation. The motion model of the EKF is derived from the foot-mounted IMU data and the measurement model from the vision system. Existing indoor positioning systems for training exercises require extensive active infrastructure installation, which is not viable for exercises taking place in a remote area. With the use of passive infrastructure (i.e., fiducial markers), the positioning system can accurately track user position over a longer duration of time and can be easily integrated into the environment. We evaluated our system on an indoor trajectory of 250 m. Results show that even with discrete corrections, near a meter level of accuracy can be achieved. Our proposed system attains the positioning accuracy of 0.55 m for a forward walk, 1.05 m for a backward walk, and 1.68 m for a sideways walk with a 90% confidence level.<\/jats:p>","DOI":"10.3390\/s20185031","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T11:24:24Z","timestamp":1599218664000},"page":"5031","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Improved Position Accuracy of Foot-Mounted Inertial Sensor by Discrete Corrections from Vision-Based Fiducial Marker Tracking"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9139-971X","authenticated-orcid":false,"given":"Humayun","family":"Khan","sequence":"first","affiliation":[{"name":"Human Interface Technology Laboratory, University of Canterbury, Christchurch 8041, New Zealand"},{"name":"Wireless Research Centre, University of Canterbury, Christchurch 8041, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6009-879X","authenticated-orcid":false,"given":"Adrian","family":"Clark","sequence":"additional","affiliation":[{"name":"School of Product Design, University of Canterbury, Christchurch 8041, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2485-8813","authenticated-orcid":false,"given":"Graeme","family":"Woodward","sequence":"additional","affiliation":[{"name":"Wireless Research Centre, University of Canterbury, Christchurch 8041, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0637-7701","authenticated-orcid":false,"given":"Robert W.","family":"Lindeman","sequence":"additional","affiliation":[{"name":"Human Interface Technology Laboratory, University of Canterbury, Christchurch 8041, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,4]]},"reference":[{"key":"ref_1","unstructured":"Woodman, O. (2010). Pedestrian Localisation for Indoor Environments. [Ph.D. Thesis, University of Cambridge]."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MCG.2005.140","article-title":"Pedestrian tracking with shoe-mounted inertial sensors","volume":"25","author":"Foxlin","year":"2005","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_3","unstructured":"PLUS Location Systems (2020, August 14). The PLUS Activate Platform. Available online: https:\/\/pluslocation.com\/solutions\/."},{"key":"ref_4","unstructured":"Ubisense (2020, August 14). Ubisense: Dimension4 UWB RTLS. Available online: https:\/\/ubisense.com\/dimension4\/."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"107193","DOI":"10.1016\/j.patcog.2019.107193","article-title":"UcoSLAM: Simultaneous localization and mapping by fusion of keypoints and squared planar markers","volume":"101","year":"2020","journal-title":"Pattern Recognit."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Seco, F., and Jimenez, A.R. (2017, January 18\u201321). Autocalibration of a wireless positioning network with a FastSLAM algorithm. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan.","DOI":"10.1109\/IPIN.2017.8115876"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Chen, X., Nixon, K.W., and Chen, Y. (2016, January 6\u20139). Practical power consumption analysis with current smartphones. Proceedings of the 29th IEEE International System-on-Chip Conference (SOCC), Seattle, WA, USA.","DOI":"10.1109\/SOCC.2016.7905505"},{"key":"ref_8","unstructured":"XSens Technologies (2019). MTi 1-Series Datasheet, Document MT0512P, XSens Technologies. rev.2019.A."},{"key":"ref_9","unstructured":"VectorNav Technologies (2020, April 23). Inertial Measurement Units and Inertial Navigation. Available online: https:\/\/www.vectornav.com\/support\/library\/imu-and-ins."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2657","DOI":"10.1109\/TBME.2010.2060723","article-title":"Zero-velocity detection\u2014An algorithm evaluation","volume":"57","author":"Skog","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Jimenez, A.R., Seco, F., Prieto, C., and Guevara, J. (2009, January 26\u201328). A comparison of pedestrian dead-reckoning algorithms using a low-cost MEMS IMU. Proceedings of the IEEE International Symposium on Intelligent Signal Processing, Budapest, Hungary.","DOI":"10.1109\/WISP.2009.5286542"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"10536","DOI":"10.3390\/s120810536","article-title":"Pedestrian navigation based on a waist-worn inertial sensor","volume":"12","author":"Alvarez","year":"2012","journal-title":"Sensors"},{"key":"ref_13","first-page":"1","article-title":"Using the ADXL202 in pedometer and personal navigation applications","volume":"2","author":"Weinberg","year":"2002","journal-title":"Anal. Devices AN-602 Appl. Note"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"6908","DOI":"10.1109\/JSEN.2018.2857502","article-title":"Step length estimation methods based on inertial sensors: A review","volume":"18","author":"Bahillo","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rantanen, J., M\u00e4kel\u00e4, M., Ruotsalainen, L., and Kirkko-Jaakkola, M. (2018, January 24\u201327). Motion context adaptive fusion of inertial and visual pedestrian navigation. Proceedings of the 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Nantes, France.","DOI":"10.1109\/IPIN.2018.8533872"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"075202","DOI":"10.1088\/0957-0233\/19\/7\/075202","article-title":"Foot mounted inertial system for pedestrian navigation","volume":"19","author":"Godha","year":"2008","journal-title":"Meas. Sci. Technol."},{"key":"ref_17","unstructured":"Kwakkel, S., Lachapelle, G., and Cannon, M. (2008, January 16\u201319). GNSS aided in situ human lower limb kinematics during running. Proceedings of the 21st International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2008), Savannah, GA, USA."},{"key":"ref_18","first-page":"5","article-title":"Pedestrian tracking using inertial sensors","volume":"3","year":"2009","journal-title":"J. Phys. Agents"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.aei.2015.10.005","article-title":"Combining visual natural markers and IMU for improved AR based indoor navigation","volume":"31","author":"Neges","year":"2017","journal-title":"Adv. Eng. Inform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Fusco, G., and Coughlan, J.M. (2018, January 11\u201313). Indoor localization using computer vision and visual-inertial odometry. Proceedings of the 2018 International Conference on Computers Helping People with Special Needs(ICCHP), Linz, Austria.","DOI":"10.1007\/978-3-319-94274-2_13"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7253","DOI":"10.1109\/JSEN.2018.2847038","article-title":"Context recognition in infrastructure-free pedestrian navigation\u2014Toward adaptive filtering algorithm","volume":"18","author":"Rantanen","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhang, G., and Bao, H. (2016, January 19\u201323). Robust keyframe-based monocular SLAM for augmented reality. Proceedings of the 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Merida, Mexico.","DOI":"10.1109\/ISMAR.2016.24"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Babu, B.W., Kim, S., Yan, Z., and Ren, L. (2016, January 19\u201323). \u03c3-dvo: Sensor noise model meets dense visual odometry. Proceedings of the 2016 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Merida, Mexico.","DOI":"10.1109\/ISMAR.2016.11"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sch\u00f6ps, T., Engel, J., and Cremers, D. (2014, January 10\u201312). Semi-dense visual odometry for AR on a smartphone. Proceedings of the 2014 IEEE international symposium on mixed and augmented reality (ISMAR), Munich, Germany.","DOI":"10.1109\/ISMAR.2014.6948420"},{"key":"ref_25","unstructured":"Yan, Z., Ye, M., and Ren, L. (2020). Dense Visual SLAM with Probabilistic Surfel Map. (10,553,026), U.S. Patent."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1109\/TPAMI.2017.2658577","article-title":"Direct sparse odometry","volume":"40","author":"Engel","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, P., Qin, T., Hu, B., Zhu, F., and Shen, S. (2017, January 9\u201313). Monocular visual-inertial state estimation for mobile augmented reality. Proceedings of the 2017 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), Nantes, France.","DOI":"10.1109\/ISMAR.2017.18"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, M., Kim, B.H., and Mourikis, A.I. (2013, January 6\u201310). Real-time motion tracking on a cellphone using inertial sensing and a rolling-shutter camera. Proceedings of the 2013 IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6631248"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ahmed, D.B., and Diaz, E.M. (2017). Loose coupling of wearable-based INSs with automatic heading. Sensors, 17.","DOI":"10.3390\/s17112534"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wagstaff, B., Peretroukhin, V., and Kelly, J. (2017, January 18\u201321). Improving foot-mounted inertial navigation through real-time motion classification. Proceedings of the 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, Japan.","DOI":"10.1109\/IPIN.2017.8115947"},{"key":"ref_31","unstructured":"STMicroelectronics (2015). LSM9DS1 Datasheet, Document DocID025715, STMicroelectronics Co.. Rev 3."},{"key":"ref_32","unstructured":"TDK InvenSense (2017). ICM-20948 Datasheet, Document DS-000189, TDK Co.. Revision: 1.3."},{"key":"ref_33","unstructured":"Bosch (2016). BNO055 Datasheet, Document BST-BNO055-DS000-14, Bosch Sensortec. Revision: 1.4."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.imavis.2018.05.004","article-title":"Speeded up detection of squared fiducial markers","volume":"76","year":"2018","journal-title":"Image Vis. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Villien, C., Frassati, A., and Flament, B. (October, January 30). Evaluation of An Indoor Localization Engine. Proceedings of the 2019 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Pisa, Italy.","DOI":"10.1109\/IPIN.2019.8911799"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2996","DOI":"10.1109\/TIM.2018.2869262","article-title":"A robust pedestrian dead reckoning system using low-cost magnetic and inertial sensors","volume":"68","author":"Shi","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.inffus.2017.04.006","article-title":"Inertial\/magnetic sensors based pedestrian dead reckoning by means of multi-sensor fusion","volume":"39","author":"Qiu","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Chen, P., Kuang, Y., and Chen, X. (2017). A UWB\/improved PDR integration algorithm applied to dynamic indoor positioning for pedestrians. Sensors, 17.","DOI":"10.3390\/s17092065"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Chen, J., Ou, G., Peng, A., Zheng, L., and Shi, J. (2018). An INS\/WiFi indoor localization system based on the Weighted Least Squares. Sensors, 18.","DOI":"10.3390\/s18051458"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5031\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:06:51Z","timestamp":1760177211000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/18\/5031"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,4]]},"references-count":39,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20185031"],"URL":"https:\/\/doi.org\/10.3390\/s20185031","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,9,4]]}}}