Utilizing Model Structure for Efficient Simultaneous Localization and Mapping for a UAV Application, Report no. LiTH-ISY-R-2836

This contribution aims at unifying two recent trends in applied particle ltering (pf). The rst trend is the major impact in simultaneous localization and mapping (slam) applications, utilizing the Fastslam algorithm. The second one is the implications of the marginalized particle lter (mpf) or the Rao-Blackwellized particle lter (rbpf) in positioning and tracking applications. Using the standard Fastslam algorithm, only low-dimensional vehicle models are computationally feasible. In this work, an algorithm is introduced which merges Fastslam and mpf, and the result is an algorithm for slam applications, where state vectors of higher dimensions can be used. Results using experimental data from a uav (helicopter) are presented. The algorithm fuses measurements from on-board inertial sensors (accelerometer and gyro) and vision in order to solve the slam problem, i.e., enable navigation over a long period of time.