Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2011;11(10):9393-410.
doi: 10.3390/s111009393. Epub 2011 Sep 29.

PDR with a foot-mounted IMU and ramp detection

Antonio R Jiménez  1 , Fernando Seco, Francisco Zampella, José C Prieto, Jorge Guevara
Affiliations

PDR with a foot-mounted IMU and ramp detection

Antonio R Jiménez et al. Sensors (Basel). 2011.

Abstract

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.

Keywords: drift elimination; indoor localization; inertial measureent unit (IMU); inertial navigation; pedestrian dead-reckoning; ramp detection.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Person walking on one of the access ramps of CAR-CSIC building. For position estimation and ramp detection, an IMU is attached to the right foot of the person using the shoe laces (orange color box).
Figure 2.
Figure 2.
Block diagram of the PDR method with corrections based on ramps’ position.
Figure 3.
Figure 3.
Ramps to access the main building of CAR-CSIC center.
Figure 4.
Figure 4.
The metric to detect ramps (ψ · Δz, Pitch multiplied by Rise): (a) Histogram of the metric for a total of 696 steps, and (b) Distribution of all detected steps on a Pitch versus Rise plot (the threshold 9 degrees·cm, used for ramp detection, is overlayed).
Figure 5.
Figure 5.
Ramp detections and associations, for 2 repetitions of a test visiting ramps 2, 3 and 4. Top graph: Estimated Pitch and Rise. Bottom graph: Ramp detections (crosses) and the identification of ramps after the association (marked with the number of the associated ramp).
Figure 6.
Figure 6.
Ramp detection over irregular terrain: (a) Photograph of the uneven trail made of stones placed over the grass; (b) Ramp detections and associations, for 2 repetitions of a test visiting ramps 1 and 5, as well as the uneven trail; top graph: Estimated Pitch and Rise; bottom graph: Ramp detections (crosses) and the identification of ramps after the association (marked with the number of the associated ramp).
Figure 7.
Figure 7.
Closed route repeated 8 times, for a trajectory with a total length of 1 km. The trajectory is along the main corridors in the CAR-CSIC building, going temporarily to an exterior yard to finally enter again in the building. (a) PDR estimation with IEZ+ algorithm (a significant drift is observed). (b) Ramp-assisted PDR estimation (the drift is eliminated).
Figure 7.
Figure 7.
Closed route repeated 8 times, for a trajectory with a total length of 1 km. The trajectory is along the main corridors in the CAR-CSIC building, going temporarily to an exterior yard to finally enter again in the building. (a) PDR estimation with IEZ+ algorithm (a significant drift is observed). (b) Ramp-assisted PDR estimation (the drift is eliminated).
Figure 8.
Figure 8.
Influence of the Ramp density on the final position error.
See this image and copyright information in PMC

References

    1. Jiménez A, Seco F, Prieto J, Roa J. Tecnologías sensoriales de localización para entornos inteligentes. I Congreso español 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.
    1. Hightower J, Borriello G. Location systems for ubiquitous computing. Computer. 2001;34:57–66.
    1. 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.
    1. Stirling R. University of Alberta; Edmonton, AB, Canada: 2004. Development of a Pedestrian Navigation System Using Shoe Mounted Sensors. PhD Thesis,
    1. Woodman OJ. An Introduction to Inertial Navigation. University of Cambridge; Cambridge, UK: 2007. Technical Report 03, Number 696.

Publication types