Rao-Blackwellized particle filtering for 6-DOF estimation of attitude and position via GPS and inertial sensors
The authors present an innovative method for the efficient joint estimation of attitude and position in six degrees of freedom via sensors such as GPS, inertial measurement units, and odometry. Traditional methods for attitude estimation via Kalman filtering are beset by many conceptual problems relating to the representation of orientations in linear spaces, leading to difficulties in implementation and the interpretation of uncertainty estimates, among other issues. These problems are compounded when it is necessary to jointly estimate position and attitude. We demonstrate how Rao-Blackwellized particle filtering provides a framework for approaching this estimation problem that is both conceptually appealing and practical. Results are shown that demonstrate the filter's robustness to sensor outages and its ability to perform well even in situations with noisy sensors and high initial uncertainty in all state dimensions; these situations are precisely those in which traditional Kalman filtering approaches are most likely to experience problems