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Exploration and mapping of unknown environments is a fundamental task inapplications for autonomous robots. In this article, we present a completeframework for deploying MAVs in autonomous exploration missions in unknownsubterranean areas. The main motive of exploration algorithms is to depict thenext best frontier for the robot such that new ground can be covered in a fast,safe yet efficient manner. The proposed framework uses a novel frontierselection method that also contributes to the safe navigation of autonomousrobots in obstructed areas such as subterranean caves, mines, and urban areas.The framework presented in this work bifurcates the exploration problem inlocal and global exploration. The proposed exploration framework is alsoadaptable according to computational resources available onboard the robotwhich means the trade-off between the speed of exploration and the quality ofthe map can be made. Such capability allows the proposed framework to bedeployed in a subterranean exploration, mapping as well as in fast search andrescue scenarios. The overall system is considered a low-complexity andbaseline solution for navigation and object localization in tunnel-likeenvironments. The performance of the proposed framework is evaluated indetailed simulation studies with comparisons made against a high-levelexploration-planning framework developed for the DARPA Sub-T challenge as itwill be presented in this article.

REF: A Rapid Exploration Framework for Deploying Autonomous MAVs in Unknown Environments