Temporary maps for robust localization in semi-static environments

Accurate and robust localization is essential for the successful navigation of autonomous mobile robots. The majority of existing localization approaches, however, is based on the assumption that the environment is static which does not hold for most practical application domains. In this paper, we present a localization framework that can robustly track a robot's pose even in non-static environments. Our approach keeps track of the observations caused by unexpected objects in the environment using temporary local maps. It relies both on these temporary local maps and on a reference map of the environment for estimating the pose of the robot. Experimental results demonstrate that by exploiting the observations caused by unexpected objects our approach outperforms standard localization methods for static environments.

Authors

• Daniel Meyer-Delius
• Jürgen Hess 0001
• Giorgio Grisetti
• Wolfram Burgard

Keywords

• Robustness
• Particle measurements
• Atmospheric measurements
• Mobile robots
• Simultaneous localization and mapping
• Trajectory
• Robust Localization
• Local Map
• Local Approach
• Mobile Robot
• Reference Map
• Environment Map
• Automated Guided Vehicles
• Robot Pose
• Non-stationary Environments
• Environmental Changes
• Continuous Distribution
• Nodes In The Graph
• Observation Error
• Particle Filter
• Pose Estimation
• Local Reference
• Goal Of Experiment
• Standard Filter
• Part Of Environment
• Static Function
• Robot Localization
• Dynamic Objects
• Particle Weight
• Large Open Spaces
• Advanced Measures
• Ratio Of Set
• Static Objects
• Robot Trajectory
• Conditions Hold