Efficient Probabilistic Approach to Range-Only SLAM With a Novel Likelihood Model

Abstract

The range-only simultaneous localization and mapping (RO-SLAM) problem is considered in this article. This is a difficult problem since bearing measurements are not available. Also, the frequent outliers induced by the sensor nature (typically sonar or radio pulses) render the RO-SLAM problem more challenging. The non-Gaussian distribution found in the RO-SLAM problem makes that the landmarks cannot be directly updated using the approach based on the Gaussian assumption. We propose a region-based particle filter and a proper likelihood model to realize the reduction of particle numbers and allow the introduction of new landmarks without delayed processing. A transition mode is designed from the loosely coupled way to the tightly coupled way and used to estimate the pose of the robot and locations of landmarks. Furthermore, two detection methods are, respectively, used to remove the outliers generated by the range-only sensors according to the distribution status of the landmark estimation. The proposed algorithm is evaluated through simulations and experiments with a range-only sensor, i.e., Ultrawideband (UWB). Furthermore, the obtained results are compared with the classical algorithm. The simulation and experiment results verify the validity and superiority of the proposed RO-SLAM algorithm.