Pattern recognition for loosely-coupled GPS/odometer fusion

Conventionally GPS receivers and odometers are used in localization systems for ground vehicles/robots due to cost constraints. When these are deployed in urban conditions, multi-path and wheel slippage often result in large localization estimation errors. In this paper, pattern recognition techniques are employed to improve the localization estimates of a loosely coupled GPS-odometer solution. The presented method filters out from the fusion process false GPS estimates and uses extensively information on the vehicle ego-state. The approach comprises three phases. First, a detection algorithm is used to recognize likely false GPS estimates, which are then excluded from Kalman Filter updates. Second, we model the vehicle motion as a weighted sum of individual maneuvers. These are processed by a multiple model Kalman Filter to improve accuracy. Third, a maneuver recognition algorithm is used to select automatically the type of motion taken by the vehicle. The performance of our localization system has been evaluated in a quantitative manner by comparing it with a reference trajectory. This reference trajectory is estimated by a localization system based on high-grade GPS-IMU-odometer. Extensive trials were performed in different real traffic conditions; results have validated the approach and demonstrated tremendous potential.