Markov Localization for Mobile Robots in Dynamic Environments

TL;DR

This paper introduces a Markov localization method for mobile robots that accurately estimates position in dynamic, noisy, and crowded environments, enabling reliable global localization and recovery from failures.

Contribution

It presents a grid-based Markov localization approach tailored for dynamic environments, capable of global localization and failure recovery in real-world robot applications.

Findings

Robust localization in crowded, dynamic environments

Effective recovery from localization failures

Validated in real-world museum tour-guide robots

Abstract

Localization, that is the estimation of a robot's location from sensor data, is a fundamental problem in mobile robotics. This papers presents a version of Markov localization which provides accurate position estimates and which is tailored towards dynamic environments. The key idea of Markov localization is to maintain a probability density over the space of all locations of a robot in its environment. Our approach represents this space metrically, using a fine-grained grid to approximate densities. It is able to globally localize the robot from scratch and to recover from localization failures. It is robust to approximate models of the environment (such as occupancy grid maps) and noisy sensors (such as ultrasound sensors). Our approach also includes a filtering technique which allows a mobile robot to reliably estimate its position even in densely populated environments in which crowds of people block the robot's sensors for extended periods of time. The method described here has been implemented and tested in several real-world applications of mobile robots, including the deployments of two mobile robots as interactive museum tour-guides.