A Kalman-filter-based multi-sensor terrain profile measurement system: principle, implementation and validation

Abstract

This paper discusses an improved design of vehicle-based mobile terrain profile measurement system that derives the terrain profile by combining information from several different sensors measuring distance, altitudes and position. The main challenge of the measurement system design is to derive the instantaneous dynamic motion of the platform vehicle in order to correct the direct profile elevation measurement from a set of laser optical sensors. By processing the velocity and attitude data from an Inertial Measurement Unit (IMU) and the absolute position data from a Global Positioning System (GPS), a Kalman Filter/Smoother algorithm is utilized in this sensor fusion application as a key step to obtain an accurate measurement of the platform vehicle's dynamic motion. Through the implementation of this approach, not only is a high accuracy of measurement during short-time vehicle dynamic motion achieved, the algorithm also eliminates a sensor drift problem associated with the long term stability of the measurement system. The hardware and software prototype of this design have been implemented, and initial field tests show that the methodology has achieved good measurement accuracy.