Towards effective localization in dynamic environments

Localization in dynamic environments is still a challenging problem in robotics - especially if rapid and large changes occur irregularly. Inspired by SLAM algorithms, our Bayesian approach to this so-called dynamic localization problem divides it into a localization problem and a mapping problem, respectively. To tackle the localization problem we use a particle filter, coupled with a distance filter and a scan matching method, which achieves a more robust localization against dynamic obstacles. For the mapping problem we use an extended sensor model which results in an effective and precise map update effect. We compare our approach against other localization methods and evaluate the impact the map update effect has on the localization in dynamic environments.

Authors

• Dali Sun
• Florian Geißer
• Bernhard Nebel

Keywords

• Hidden Markov models
• Simultaneous localization and mapping
• Heuristic algorithms
• Computational modeling
• Bayes methods
• Dynamic Environment
• Local Method
• Local Dynamics
• Local Problems
• Particle Filter
• Sensor Model
• Update Function
• Mapping Problem
• Robust Localization
• Problem In Robotics
• Dynamic Obstacles
• Environmental Changes
• Dynamic Changes
• Transition State
• Hidden Markov Model
• Mapping Results
• Functional Dynamics
• Explicit Model
• Iteration Step
• Real-world Environments
• State Transition Model
• Robot Pose
• Dynamic Objects
• Occupancy Grid
• Laser Ranging
• State Transition Probability
• Odometry
• Matching Score
• Wrong Estimation
• Position Of The Robot