Multi-sensor data fusion for land vehicle localization using RTMAPS

In this paper we present the use of ^RTMAPS in multi-sensor data fusion for land vehicle localization. The GPS, INS and odometers data time synchronisation and data logging and processing are performed in real time using our software ^RTMAPS. The fusion algorithm based on Kalman filtering integrates information coming from a single GPS receiver with inertial navigation system and wheel speed encoders. The filter is used to enhance positioning accuracy, especially during periods of losses of GPS signal and to achieve submeter accuracy for the positioning of a land vehicle. The results prove that ^RTMAPS is a good framework for prototyping multi-sensor automotive application. The integration results show that this system can be used as an autonomous localisation system for more than 10 minutes of GPS data outage.