A statistical approach to simultaneous mapping and localization for mobile robots

TL;DR

This paper introduces a Bayesian statistical sampling method for simultaneous mapping and localization (SLAM) in indoor environments, improving robot navigation by estimating the posterior distribution of maps from sensor data.

Contribution

It presents a novel importance sampling algorithm for SLAM that leverages Bayesian factorization to efficiently estimate the map and position posterior distribution.

Findings

Effective in real indoor environments

Uses range finder and odometer data

Demonstrates improved mapping accuracy

Abstract

Mobile robots require basic information to navigate through an environment: they need to know where they are (localization) and they need to know where they are going. For the latter, robots need a map of the environment. Using sensors of a variety of forms, robots gather information as they move through an environment in order to build a map. In this paper we present a novel sampling algorithm to solving the simultaneous mapping and localization (SLAM) problem in indoor environments. We approach the problem from a Bayesian statistics perspective. The data correspond to a set of range finder and odometer measurements, obtained at discrete time instants. We focus on the estimation of the posterior distribution over the space of possible maps given the data. By exploiting different factorizations of this distribution, we derive three sampling algorithms based on importance sampling. We illustrate the results of our approach by testing the algorithms with two real data sets obtained through robot navigation inside office buildings at Carnegie Mellon University and the Pontificia Universidad Catolica de Chile.