Generalized best-first search strategies and the optimality of A*
Journal of the ACM (JACM)
A frontier-based approach for autonomous exploration
CIRA '97 Proceedings of the 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation
Monte Carlo localization in outdoor terrains using multilevel surface maps
Journal of Field Robotics - Special Issue on Field and Service Robotics
Exploring unknown environments with mobile robots using coverage maps
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Active-semantic localization with a single consumer-grade camera
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Topological map induction using neighbourhood information of places
Autonomous Robots
Hierarchical SLAM: Real-Time Accurate Mapping of Large Environments
IEEE Transactions on Robotics
Large-Scale SLAM Building Conditionally Independent Local Maps: Application to Monocular Vision
IEEE Transactions on Robotics
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For an accurate and efficient exploration, a local map-based exploration strategy is proposed. Segmented frontiers and relative transformations constitute a tree structure; using frontier segmentation and a local map management method, a robot can expand the mapped environment by moving along the tree structure. Although this local map-based exploration method uses only local maps and adjacent node information, mapping completion and efficiency can be greatly improved by merging and updating the frontier nodes. Simulation results demonstrate that the computational time does not increase during the exploration process, or when the resulting map becomes large. Additionally, the resulting path is effective in reducing the uncertainty in simultaneous localization and mapping or localization because of the loop-inducing characteristics from the child node to the parent node.