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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 stateof-the-art.
Cao et al. (Sun,) studied this question.