Indoor Navigation Using Information From A Map And A Rangefinder

TL;DR

This paper presents a Bayesian-based nonlinear filtering algorithm for indoor navigation that utilizes map data and rangefinder measurements to accurately estimate object positions.

Contribution

It introduces a novel filtering approach using the point-mass method for optimal indoor navigation based on distance measurements and map information.

Findings

The proposed algorithm improves position estimation accuracy.

Simulation results demonstrate the effectiveness of the Bayesian filtering approach.

The method outperforms traditional techniques in indoor navigation scenarios.

Abstract

The problem of indoor navigation of mobile objects, using a map and measurements of distances to the walls is considered. A nonlinear filtering problem aimed at calculating the optimal, in the root-mean-square sense, of the sought parameters is formulated in the context of the Bayesian approach. The algorithm for its solution based on the point-mass method is described. The simulation results illustrating the advantages of the proposed problem statement and the resultant algorithm are discussed.