A biologically inspired solution to simultaneous localization and consistent mapping in dynamic environments

Keyphrases

• Dynamic Environment 100%
• Biologically Inspired 100%
• Simultaneous Localization 100%
• Consistent Mapping 100%
• Dynamic Properties 40%
• Shunt 40%
• Classification Basis 40%
• Neural Network 20%
• Steady State 20%
• Utilization Rate 20%
• Computational Complexity 20%
• Filter Method 20%
• Indoor Environment 20%
• Environmental Change 20%
• Recursive Filter 20%
• Mapping Problem 20%
• Matching Mechanism 20%
• Static Environment 20%
• Lyapunov Stability 20%
• Model Selection 20%
• Extended Kalman Filter 20%
• Dynamic Object 20%
• Observation Information 20%
• Biological Basis 20%
• Selection Parameters 20%
• Misclassification 20%
• High Utilization 20%
• Discrimination Algorithm 20%
• Static Object 20%
• Memory-based Methods 20%
• Map Representation 20%
• Scan Matching 20%
• Dynamic Target 20%
• Mobile Robotics 20%
• Filtering Divergence 20%
• Memory Neural Network 20%
• Robot Base 20%
• Static-dynamic 20%

Engineering

• Adaptability 100%
• Term Memory 100%
• Dynamic Property 100%
• Misclassification 50%
• IIR Filter 50%
• Kalman Filter 50%
• Periodic Time 50%
• Lyapunov Stability 50%
• Dynamic Object 50%
• Robot 50%
• Computational Complexity 50%

Computer Science

• Dynamic Environment 100%
• Neural Network 40%
• Dynamic Property 40%
• Simulated Environment 20%
• Mobile Robotics 20%
• Unsolved Problem 20%
• Static Environment 20%
• Robot 20%
• Computational Complexity 20%
• Fundamental Problem 20%
• Biological Base 20%
• Lyapunov Stability 20%
• Kalman Filter 20%