The localization of persons in indoor environments is nowadays an open problem. There are partial solutions based on the deployment of a network of sensors (Local Positioning Systems or LPS). Other solutions only require the installation of an inertial sensor on the person’s body (Pedestrian Dead-Reckoning or PDR). PDR solutions integrate the signals coming from an Inertial Measurement Unit (IMU), which usually contains 3 accelerometers and 3 gyroscopes. The main problem of PDR is the accumulation of positioning errors due to the drift caused by the noise in the sensors. This paper presents a PDR solution that incorporates a drift correction method based on detecting the access ramps usually found in buildings. The ramp correction method is implemented over a PDR framework that uses an Inertial Navigation algorithm (INS) and an IMU attached to the person’s foot. Unlike other approaches that use external sensors to correct the drift error, we only use one IMU on the foot. To detect a ramp, the slope of the terrain on which the user is walking, and the change in height sensed when moving forward, are estimated from the IMU. After detection, the ramp is checked for association with one of the existing in a database. For each associated ramp, a position correction is fed into the Kalman Filter in order to refine the INS-PDR solution. Drift-free localization is achieved with positioning errors below 2 meters for 1,000-meter-long routes in a building with a few ramps.
References
[1]
Jiménez, A; Seco, F; Prieto, J; Roa, J. Tecnologías sensoriales de localización para entornos inteligentes. I Congreso espa?ol de informática—Simposio de Computación Ubicua e Inteligencia Ambiental. International Symposium of Ubiquitous Computing and Ambient Intelligence, Granada, Spain, 14–16 September 2005; pp. 75–86.
[2]
Hightower, J; Borriello, G. Location systems for ubiquitous computing. Computer 2001, 34, 57–66.
[3]
Ojeda, L; Borenstein, J. Personal Dead-reckoning System for GPS-denied Environments. Proceedings of the IEEE International Workshop on Safety, Security and Rescue Robotics, Rome, Italy, 27–29 September 2007; pp. 1–6.
[4]
Stirling, R. Development of a Pedestrian Navigation System Using Shoe Mounted SensorsPhD Thesis. University of Alberta, Edmonton, AB, Canada, 2004.
[5]
Woodman, OJ. An Introduction to Inertial Navigation. Technical Report 03, Number 696; University of Cambridge: Cambridge, UK, 2007.
[6]
Jiménez, A; Seco Granja, F; Prieto Honorato, J; Guevara Rosas, J. Pedestrian Indoor Navigation by Aiding a Foot-mounted IMU with RFID Signal Strength Measurements. Proceedings of the 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Zürich, Switzerland, 15–17 September 2010; p. 7.
[7]
Ascher, C; Keber, C; Trommer, G. Combining Laser Range Measurement and Dual-IMU IPNS for Precise Indoor SLAM. Proceedings of the Symposium Gyro Technology, Karlsruhe, Germany, 22–23 September 2009; pp. 1–23.
[8]
Borenstein, J; Ojeda, L; Kwanmuang, S. Heuristic reduction of Gyro drift for personnel tracking systems. J. Navig 2008, 62, 41–58.
[9]
Woodman, O; Harle, R. Pedestrian Localisation for Indoor Environments. Proceedings of the 10th International Conference on Ubiquitous Computing—UbiComp’08, Seoul, Korea, 21–24 September 2008; pp. 1–10.
[10]
Vera-Nadales, MJ. Recognition of Human Motion Related Activities from SensorsPhD Thesis. University of Malaga, Malaga, Spain, 2010.
[11]
Korbinian, F; Vera-Nadales, MJ; Robertson, P; Angermann, M. Reliable Real-Time Recognition of Motion Related Human Activities Using MEMS Inertial Sensors. ION GNSS’10, Portland, OR, USA, 21–24 September 2010; pp. 1–14.
[12]
Altun, K; Barshan, B; Tun?el, O. Comparative study on classifying human activities with miniature inertial and magnetic sensors. Pattern Recog 2010, 43, 3605–3620.
[13]
Gusenbauer, D; Isert, C; Kr?sche, J. Self-Contained Indoor Positioning on Off-The-Shelf Mobile Devices. International Conference on Indoor Positioning and Indoor Navigation, Hoenggerberg, Switzerland, 15–17 September 2010; pp. 15–17.
[14]
Kourogi, M; Ishikawa, T; Kurata, T. A method of pedestrian dead reckoning using action recognition. Proceedings of the IEEE/ION Position Location and Navigation Symposium (PLANS’10), Indian Wells, CA, USA, 4–6 May 2010; pp. 85–89.
[15]
Wagner, J; Isert, C; Purschwitz, A; Kistner, A. Improved Vehicle Positioning for Indoor Navigation in Parking Garages through Commercially Available Maps. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation (IPIN’10), Zurich, Switzerland, 15–17 September 2010; pp. 875–882.
[16]
Foxlin, E. Pedestrian tracking with shoe-mounted inertial sensors. IEEE Comput. Graph. Appl 2005, 25, 38–46.
[17]
Jiménez, A; Seco, F; Prieto, J; Guevara, J. Indoor Pedestrian Navigation Using an INS/EKF framework for Yaw Drift Reduction and a Foot-mounted IMU. Proceedings of the 7th Workshop on Positioning, Navigation and Communication, WPNC’10, Dresden, Germany, 11–12 March 2010; 10, pp. 135–143.
[18]
Jiménez, A; Seco, F; Zampella, F; Prieto, JC; Guevara, J. Localización inercial de personas con detección de rampas. Seminario Anual de Automática, Electrónica Industrial e Instrumentación (SAAEI)Sesión Especial 2: Ambientes Inteligentes. 2011, 807–812.
[19]
Konvalin, BC. Compensating for tilt, hard-iron and soft-iron effects. Sens Mag 2009, 1–11.
[20]
Renaudin, V; Gilliéron, PY. Personal robust navigation in challenging applications. J. Navig 2011, 64, 235–249.