Improvement in Monte Carlo localization using information theory and statistical approaches

Keyphrases

• Probability Density Function 100%
• System Dynamics 100%
• Particle Filter 100%
• Information Theory 100%
• Monte Carlo Localization 100%
• Accuracy Improvement 50%
• Localization Algorithm 50%
• Dynamic Behavior 50%
• Internal State 50%
• Information Entropy 50%
• Mobile Robot 50%
• System State 50%
• Simulation Experiment 50%
• Process-based 50%
• Robot Position 50%
• Filter Performance 50%
• Mutation Operator 50%
• Mutation Strategy 50%
• Lithium-ion Battery 50%
• Observational Data 50%
• Local Planner 50%
• Monte Carlo Localization Algorithm 50%
• Hidden Markov Process 50%
• New mutation 50%
• Posterior Probability Density 50%
• Recursive Bayesian Estimation 50%
• Probability-based Approach 50%
• Localization Method 50%

Computer Science

• localization algorithm 100%
• Information Theory 100%
• Statistical Approach 100%
• Probability Density Function 66%
• Particle Filter 66%
• Internal State 33%
• Robot 33%
• Mobile Robot 33%
• Simulation Experiment 33%
• Posterior Probability 33%
• Mutation Operator 33%
• Markov Process 33%
• Dynamic Behavior 33%
• Bayes Estimator 33%
• Probability 33%
• Entropy 33%

Mathematics

• Monte Carlo 100%
• Statistical Approach 100%
• Information Theory 100%
• Probability Density Function 66%
• Approximates 33%
• System State 33%
• Observed Data 33%
• Markov Process 33%
• Test Result 33%
• Bayesian Estimation 33%
• Probability 33%
• Entropy 33%
• Likelihood 33%
• Internal State 33%
• Observation Data 33%