REAL: Rapid Exploration with Active Loop-Closing toward Large-Scale 3D Mapping using UAVs

Abstract

Exploring an unknown environment without colliding with obstacles is one of the essentials of autonomous vehicles to perform diverse missions such as structural inspections, rescues, deliveries, and so forth. Therefore, unmanned aerial vehicles (UAVs), which are fast, agile, and have high degrees of freedom, have been widely used. However, previous approaches have two limitations: a) First, they may not be appropriate for exploring large-scale environments because they mainly depend on random sampling-based path planning that causes unnecessary movements. b) Second, they assume the pose estimation is accurate enough, which is the most critical factor in obtaining an accurate map. In this paper, to explore and map unknown large-scale environments rapidly and accurately, we propose a novel exploration method that combines the pre-calculated Peacock Trajectory with graphbased global exploration and active loop-closing. Because the two-step trajectory that considers the kinodynamics of UAVs is used, obstacle avoidance is guaranteed in the recedinghorizon manner. In addition, local exploration that considers the frontier and global exploration based on the graph maximizes the speed of exploration by minimizing unnecessary revisiting. In addition, by actively closing the loop based on the likelihood, pose estimation performance is improved. The proposed method's performance is verified by exploring 3D simulation environments in comparison with the state-of-the-art methods. Finally, the proposed approach is validated in a real-world experiment.

Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots

“…A rich body of work has focused on the problems of autonomous single and multi-robot exploration [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Early work in single-robot exploration included the sampling of "next-best-views" [25], and the detection of frontiers [26], while recent efforts have focused on powerful planning techniques such as random trees and graphs possibly combined with volumetric calculations [22,23,[36][37][38][39], receding horizon techniques [23], multi-objective optimization [36,37], information-theoretic schemes [40], learning-based methods [41], and approaches that account for the likelihood of accumulating localization drift [22,42]. In multi-robot exploration, the seminal work in [30] presented a strategy for multi-robot coordination exploiting a grid map and a planning policy that tries to minimize the collective exploration time by considering both the cost of reaching a certain frontier cell and the "exploration utility" of each such cell as a function of the number of robots moving to that cell.…”

Exploration with Global Consistency Using Real-Time Re-integration and Active Loop Closure

“…The quadrotor will be detached from local ALC clusters if no loop closure is successfully detected within a certain period, which prevents distraction from exploration the task. Note that we may evaluate the possibility of loop-closure for historical viewpoints as [37] does, which is left as a future work.…”

Autonomous Teamed Exploration of Subterranean Environments using Legged and Aerial Robots

“…A rich body of work has focused on the problems of autonomous single and multi-robot exploration [21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Early work in single-robot exploration included the sampling of "next-best-views" [25], and the detection of frontiers [26], while recent efforts have focused on powerful planning techniques such as random trees and graphs possibly combined with volumetric calculations [22,23,[36][37][38][39], receding horizon techniques [23], multi-objective optimization [36,37], information-theoretic schemes [40], learning-based methods [41], and approaches that account for the likelihood of accumulating localization drift [22,42]. In multi-robot exploration, the seminal work in [30] presented a strategy for multi-robot coordination exploiting a grid map and a planning policy that tries to minimize the collective exploration time by considering both the cost of reaching a certain frontier cell and the "exploration utility" of each such cell as a function of the number of robots moving to that cell.…”