Hierarchical simultaneous localization and mapping

This paper presents a novel method of combining topological and feature-based mapping strategies to create a hierarchical approach to simultaneous localization and mapping (SLAM). More than simply running both processes in parallel, we use the topological mapping procedure to organize local feature-based methods. The result is an autonomous exploration and mapping strategy that scales well to large environments and higher dimensions while confronting the issue of obstacle avoidance. We have obtained successful results of our approach in an area spanning 5000 square meters.

Authors

• Brad Lisien
• Deryck Morales
• David Silver 0002
• George Kantor
• Ioannis M. Rekleitis
• Howie Choset

Keywords

• Simultaneous localization and mapping
• Orbital robotics
• Topology
• Large-scale systems
• Computational complexity
• Silver
• Grid computing
• Feature extraction
• Robot localization
• Organizing
• Hierarchical Map
• High-dimensional
• Mapping Strategy
• Obstacle Avoidance
• Exploration Strategy
• Area Approach
• Feature-based Methods
• Topological Map
• Covariance Matrix
• Optimal Control
• Feature Maps
• Free Space
• Kalman Filter
• Path Planning
• Local Map
• Global Map
• Mahalanobis Distance
• Coordinate Frame
• Extended Kalman Filter
• Start Location
• Environment Size
• Landmark Localization
• Presence Of Obstacles
• Robot Path
• Global Localization
• Graph Topology
• Standard Kalman Filter
• Neighboring Nodes