A composite beacon initialization for EKF range-only SLAM

Abstract

Accurately localize a mobile vehicle with an easy and quickly deployable system can be very useful for many applications. Herein we present an EKF-SLAM algorithm which allows using radio frequency (RF) beacons without any prior knowledge of their location. As RF beacons provide only range information, recovering their positions is not an easy task. For this range-only SLAM case, a new procedure to instantiate the beacons in the filter is proposed. The method uses two range measurements from different robot's positions to initialize two hypotheses for the beacon's location, which are then integrated in the filter as a Gaussian mixture. This approach is evaluated and compared to other initialization techniques in simulation and with a real dataset. The results show that our approach performs as well as the other existing methods for both trajectory and map errors with a low computational cost.

A Distributed Radio Beacon/IMU/Altimeter Integrated Localization Scheme with Uncertain Initial Beacon Locations for Lunar Pinpoint Landing

“…On the basis of this research, Felipe et al [ 47 ] proposed a centralized EKF-based initialization framework, which allowed the integration of inter-beacon measurements, and they enhanced it with a outlier rejection filter [ 48 , 49 ]. Geneve et al [ 50 ] proposed a short-delayed composite initialization method based on Euclidean parameterization and a two-hypothesis GMM, which showed a lower computational cost.…”

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

“…In the case of Gaussian filters, authors tend to use two common approaches: the first and most common consists on a delayed initialization of the Gaussian filter based on a pre-estimated position of landmarks [23,21,24] and the second approach uses undelayed initialization based on multi-hypotheses frameworks to cope with the non-Gaussian distribution of landmark positions. However, in delayed initialization approaches, single estimation convergence will always depend on the robot's trilateration with respect the landmark so that important delays might be produced until these landmarks converge and can be integrated in the Gaussian filter used to refine the robot position.…”

Range-Only Bearing Estimator for Localization and Mapping

“…Various methods have been proposed in the literature to solve the problem using extended Kalman filter SLAM (EKF-SLAM) [3], [6], [7], [8], [9], unscented Kalman filter SLAM (UKF-SLAM) [1] and particle filter SLAM (PF-SLAM) [10]. These methods work well locally, which can introduce a practical issue in their deployment.…”

“…In general, there are two approaches for the initialization in literature: the delayed and undelayed approaches [6]. In the first approach, the application of the estimation filter is delayed until consistent estimates of the positions for the landmarks are obtained through multiple measurements (such as, the triangulation techniques [3], [7]). This approach can lead to convergence problems particularly when the delay is set too large.…”