Dependable localization strategy in dynamic real environments

Due to dynamic changes of an environment and various kinds of uncertainties in a real world, mobile robot localization is difficult to be solved by a single continuous algorithm. In order to achieve a practical localization solution generally, this paper proposes a strategy to deal with various uncertainties using explicit discretization of robot's status. Discrete status of localization is designed with three criteria as follows: (i) polygonal environment and non-polygonal environment; (ii) static environment and dynamic environment; and (iii) global positioning problem and local tracking problem are defined. An appropriate strategy is adopted according to the robot's status. The feasibility of the proposed method is demonstrated by simulation results.

Authors

• Dongheui Lee
• Woojin Chung

Keywords

• Uncertainty
• Gaussian noise
• Mobile robots
• Iterative closest point algorithm
• Iterative algorithms
• Robot sensing systems
• Orbital robotics
• Particle filters
• Mechanical engineering
• Iterative methods
• Dynamic Environment
• Simulation Results
• Local Problems
• Mobile Robot
• Single Algorithm
• Tracking Problem
• Static Environment
• Kind Of Uncertainty
• Local Tracking
• Distribution Of Variables
• Sensor Data
• Kalman Filter
• Local Method
• Position Error
• Local Algorithm
• Distribution Of Positions
• Position Estimation
• Particle Filter
• Localizer
• Reference Position
• Update Phase
• Iterative Closest Point
• Cross-correlation Function
• Hough Transform
• Sensor Readings
• Position Of The Robot
• Position Tracking
• Pre-defined Threshold
• Sensor Model
• Sample Distribution