An efficient approach for undelayed range-only SLAM based on Gaussian mixtures expectation

This paper deals with range-only simultaneous localization and mapping (RO-SLAM), which is of particular interest in aerial robotics where low-weight range-only devices can provide a complementary continuous estimation between robot and landmarks when using radio-based sensors. Range-only sensors work at greater distances when compared to other commonly used sensors in aerial robotics and they are low-cost. However, the spherical shell uniform distribution inherent to range-only observations poses significant technological challenges, restricting the approaches that can be used to solve this problem. This paper presents an undelayed multi-hypothesis Extended Kalman Filter (EKF) approach based on Gaussian Mixture Models (GMM) and a reduced parameterization of the state vector to improve its efficiency. The paper also proposes a new robot-to-landmark and landmark-to-landmark range-only observation model for EKF-SLAM which takes advantage of the reduced parameterization. Finally, a new scheme is proposed for updating hypothesis weights based on an independence of beacon parameters. The method is firstly validated with simulations comparing the results with other state-of-the-art methods and later validated with real experiments for 3D RO-SLAM using several radio-based range-only sensors and an aerial robot.