TARE: A Hierarchical Framework for Efficiently Exploring Complex 3D Environments

We present a method for autonomous exploration in complex three-dimensional (3D) environments. Our method demonstrates exploration faster than the current state-of-the-art using a hierarchical framework — one level maintains data densely and computes a detailed path within a local planning horizon; while another level maintains data sparsely and computes a coarse path at the global scale. Such a framework shares the insight that detailed processing is most effective close to the robot; and gains computational speed by trading-off computation of details far away from the robot. The method optimizes an overall exploration path with respect to the length of the path. In addition; the path in the local area is kinodynamically feasible for the vehicle to follow at a high speed. In experiments; our systems autonomously explore indoor and outdoor environments at a high degree of complexity; with ground and aerial robots. The method produces 80% more exploration efficiency; defined as the average explored volume per second through a run; and consumes less than 50% of computation compared to the state-of-the-art.