Markov Localization for Mobile Robots in Dynamic Environments

Citations Over TimeTop 1% of 1999 papers

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.

Related Papers

• → Text-MCL: Autonomous Mobile Robot Localization in Similar Environment Using Text-Level Semantic Information(2022)24 cited
• → Pose Uncertainty in Occupancy Grids through Monte Carlo Integration(2014)7 cited
• → Pose uncertainty in occupancy grids through Monte Carlo integration(2013)4 cited
• → Probabilistic Model-Based Global Localization in an Airport Environment(2021)
• → Improved Monte Carlo Localization Based on Probability Density Maps for Autonomous Mobile Robots(2023)