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

Abstract

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.

Integrity – A Topic for Photogrammetry ?

“…Remaining systematic errors are also treated stochastically. A more natural approach could be using interval mathematics that just bound the remaining errors (Jaulin et al, 2001), for GNSS (Schön, 2016, Schön, Kutterer, 2005, Drevelle, Bonnifai, 2009, Dbouk, Schön, 2020, for images (Rohou et al, 2017, Kenmogne et al, 2018, for car navigation (Wörner et al, 2016). A state propagation via Kalman filtering is described in (Xiong et al, 2013, Chen et al, 1997.…”