Interval-Based Cooperative Uavs Pose Domain Characterization from Images and Ranges

An interval-based approach to cooperative localization for a group of unmanned aerial vehicles (UAVs) is proposed. It computes a pose uncertainty domain for each robot, i.e., a set that contains the true robot pose, assuming bounded error measurements. The algorithm combines distances measurements to the ground station and between UAVs, with the tracking of known landmarks in camera images, and provides a guaranteed enclosure of the robots pose domains. Pose uncertainty domains are computed using interval constraint propagation techniques, thanks to a branch and bound algorithm. We show that the proposed method also provides a good point estimate, that can be further refined using nonlinear iterative weighted least squares. Results are presented for simulated two-robots configurations, for experimental data, and compared with a classical Extended Kalman Filter.