Special Issue on Probabilistic Robotics and SLAM

Intelligent mobile robots need self-localization, map generation, and the ability to explore unknown environments autonomously. Probabilistic processing can be applied to overcome the problems of movement uncertainties and measurement errors. Probabilistic robotics and simultaneous localization and mapping (SLAM) technologies are therefore strongly related, and they have been the focus of many studies. As more and more practical applications are found for intelligent mobile robots, such as for autonomous driving and cleaning, the applicability of these techniques has been increasing. In this special issue, we provide a wide variety of very interesting papers ranging from studies and developments in applied SLAM technologies to fundamental theories for SLAM. There are five academic papers, one each on the following topics: first visit navigation, controls for following rescue clues, indoor localization using magnetic field maps, a new solution for self-localization using downhill simplex method, and object detection for long-term map management through image-based learning. In addition, in the next number, there will be a review paper by Tsukuba University’s Prof. Tsubouchi, who is famous for the Tsukuba Challenge and research related to mobile robotics. We editors hope this special issue will help readers to develop mobile robots and use SLAM technologies and probabilistic approaches to produce successful applications.

Gaussian Kernel Posterior Elimination for Fast Look-Ahead Rao-Blackwellised Particle Filtering for SLAM

In this paper, we explore a method for posterior elimination for fast computation of the look-ahead Rao-Blackwellised Particle Filtering (Fast la-RBPF) algorithm for the simultaneous localization and mapping (SLAM) problem in the probabilistic robotics framework. In the case when a lot of SLAM states need to be estimated, large posterior states associated with the correct state may be outnumbered by multiple non-zero smaller posteriors. We show that by masking the low posterior weight states with a Gaussian kernel prior to weight selection the accuracy of the la-RBPF SLAM algorithm can be improved. Simulation results reveal that integrated with the proposed method the fast la-RBPF SLAM performance is enhanced over both the existing RBPF SLAM and the unmodified la-RBPF SLAM algorithms.

Statistical Bases

This is the fourth and last chapter of the first section. As chapter 3 introduced the mathematical tools of probability theory needed to understand all the concepts in the book, chapter 4 does the same concerning statistics. It fills the gap between probability theory and real data coming from stochastic processes, highlighting the great amount of potential applications of the different fields of statistics—particularly estimation theory—in state-of-the-art science and engineering. Topics covered in this chapter include the fundamental tools needed in probabilistic robotics: probabilistic convergence, theory of estimators, hypothesis tests, etc. Special stress is on recursive Bayesian estimators, due to their central role in the problems of probabilistic robot localization and mapping.