Mobile robotics in a random finite set framework

Authors:
John Mullane;Ba-Ngu Vo;Martin Adams;Ba-Tuong Vo
Affiliations:
Nanyang Technological University, Singapore;University of Western Australia, Perth, Australia;University of Chile, Santiago, Chile;University of Western Australia, Perth, Australia
Venue:
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Year:
2011

Citing 7
Cited 0

Estimating uncertain spatial relationships in robotics

Autonomous robot vehicles
FastSLAM: A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics (Springer Tracts in Advanced Robotics)

FastSLAM: A Scalable Method for the Simultaneous Localization and Mapping Problem in Robotics (Springer Tracts in Advanced Robotics)
Statistical Multisource-Multitarget Information Fusion

Statistical Multisource-Multitarget Information Fusion
FISST-SLAM: Finite Set Statistical Approach to Simultaneous Localization and Mapping

International Journal of Robotics Research
The Gaussian Mixture Probability Hypothesis Density Filter

IEEE Transactions on Signal Processing
Sparse Local Submap Joining Filter for Building Large-Scale Maps

IEEE Transactions on Robotics
A Random-Finite-Set Approach to Bayesian SLAM

IEEE Transactions on Robotics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes the Random Finite Set approach to Bayesian mobile robotics, which is based on a natural multi-object filtering framework, making it well suited to both single and swarm-based mobile robotic applications. By modeling the measurements and feature map as random finite sets (RFSs), joint estimates the number and location of the objects (features) in the map can be generated. In addition, it is shown how the path of each robot can be estimated if required. The framework differs dramatically from existing approaches since both data association and feature management routines are integrated into a single recursion. This makes the framework well suited to multi-robot scenarios due to the ease of fusing multiple map estimates from swarm members, as well as mapping robustness in the presence of other mobile robots which may induce false map measurements. An overview of developments thus far is presented, with implementations demonstrating the merits of the framework on simulated and experimental datasets.