Volume XLII-1
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 315-322, 2018
https://doi.org/10.5194/isprs-archives-XLII-1-315-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-1, 315-322, 2018
https://doi.org/10.5194/isprs-archives-XLII-1-315-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

  26 Sep 2018

26 Sep 2018

ULTRASONIC BASED HEADING ESTIMATION FOR AIDING LAND VEHICLE NAVIGATION IN GNSS DENIED ENVIRONMENT

M. Moussa1, A. Moussa1,2, and N. El-Sheimy1 M. Moussa et al.
  • 1Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada
  • 2Department of Electrical Engineering, Port-Said University, Port Said, Egypt

Keywords: Ultrasonic Sensor, Land Vehicle Navigation, Inertial Navigation System, Multi-sensor, Extended Kalman Filter

Abstract. This paper introduces a novel approach for land vehicles navigation in GNSS denied environment by aiding the Inertial Navigation System (INS) with a very low-cost ultrasonic sensor using Extended Kalman Filter (EKF) to bound its drift during GNSS blockage through a heading change update to enhance the navigation estimation.

The ultrasonic sensor is mounted on the body of the car facing the direction of the car motion and behind the front right wheel, a wooden surface is mounted on the car body on the other side of this wheel with a constant distance between the sensor and this surface. The ultrasonic sensor measures this range as long as the car moving straight. When orientation changes, the ultrasonic sensor senses the range to the front right wheel. The relation between the range and the estimated GNSS/INS change of heading during GNSS availability is estimated through a linear regression model. During GNSS signal outage, the ultrasonic sensor provides heading change update to the INS standalone navigation solution.

Experimental road tests were performed, and the results show that the navigation states estimation using the proposed aiding is improved compared with INS standalone navigation solution during GNSS signal outage. For multiple GNSS outages of 60 seconds, the inclusion of the proposed update reduced the position RMSE to around 80 % of its value when using the non-holonomic constraints and velocity update only.