This paper presents an approach for decentralized range-only simultaneous localization and mapping (RO-SLAM) of a network of aerial vehicles and a set of static range-only sensors deployed in the environment. The paper makes use of a multi-hypothesis framework developed by the authors [1] in order to deal with the multiple hypotheses that are present in the early stages of the undelayed RO-SLAM, this paper extends the approach to consider the integration of landmark estimations provided by other aerial vehicles nearby the robot. The method will enable a significant reduction in the convergence time needed to remove wrong localization hypotheses for every range-only landmark and, as a results, a map with improved accuracy. The proposed approach is validated first in simulations and later on with real experiments involving real range-only sensors and two unmanned aerial vehicles.

This paper presents a new approach for aerial robots simultaneous localization and mapping (SLAM) oriented to aerial manipulation applications. The approach is based on the integration of range-only measurements and visual markers detected with the on-board camera. A multiple hypotheses framework is applied for range-only together with visual markers SLAM. This approach allows integrating two different types of sensors that are complementary for localization, mixing the stable and continuous estimation provided by range sensors with the precise but infrequent measurements from the visual markers. Real experiments involving an aerial robot and radio/visual markers have been used to validate the approach.

This paper presents an undelayed range-only simultaneous localization and mapping (RO-SLAM) based on the Extended Kalman filter. The approach is optimized for working in 3D scenarios, reducing the required computational payload at two levels: first, using a reduced spherical state vector parametrization and, second, proposing a new EKF update scheme. The paper proposes a state vector parametrization based on Gaussian-Mixture to cope with the multi-modal nature of range-only measurements and a reduced spherical parametrization of the range sensor positions that allows to shorten the length of the state vector for a given number of hypotheses. The approach is firstly tested and discussed in simulation, followed by experimental results involving a real robot and radio-based range sensors.