Undelayed 3D RO-SLAM based on Gaussian-mixture and reduced spherical parametrization

Abstract

Abstract-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.

Robust range-only SLAM for aerial vehicles

“…The method presented in this paper extends the RO-SLAM method introduced by authors in [1] making it more robust against range measurement outliers and improving the measurement correction by considering a different model parametrization for each beacon. This method uses a centralized EKF to estimate not only the 3D position of the UAV, but also the 3D position of each static beacon.…”

“…This solution presents accurate results but does not allow the integration of inter-beacon measurements in such a way that cross correlations between beacons can be taken into account. Thus, a previous work of the authors of this paper [1] proposes an undelayed 3D RO-SLAM method based on a centralized EKF-SLAM framework which allows the integration of inter-sensor measurements considering the cross correlation between them. The main problem of this solution is that, since it is based on an EKF, the solution is very sensible to the presence of measurement outliers so, in this work, a robust filter is proposed to avoid the divergence of the EKF filter.…”

“…The main contributions of this paper are the use of a twostep pre-filtering method which improve the robustness of the algorithm presented in a previous work of the authors [1] by rejecting outliers with a simple and efficient algorithm. This pre-filtering method is based on some heuristics which depends on on the current state of the UAV position and the previous range measurements received.…”

“…This pre-filtering method is based on some heuristics which depends on on the current state of the UAV position and the previous range measurements received. The second contribution of this paper is an extension of [1] to model the range measurements model, the method estimates the model parameters including this parameters in the EKF estimation. The methods are validated in simulation.…”

Robust Range-Only Mapping via Sum-of-Squares Polynomials

“…Later on, Caballero et al (2010) improved the formulation with a mixture of Gaussians to obtain an undelayed initialization of the beacons in the filter. The formulation was then extended to the 3D case with a spherical representation (Fabresse et al, 2013). Blanco et al (2008) follow the same idea but using a particle filter framework instead.…”