APACE: Agile and Perception-Aware Trajectory Generation for Quadrotor Flights
arxiv(2024)
摘要
Various perception-aware planning approaches have attempted to enhance the
state estimation accuracy during maneuvers, while the feature matchability
among frames, a crucial factor influencing estimation accuracy, has often been
overlooked. In this paper, we present APACE, an Agile and Perception-Aware
trajeCtory gEneration framework for quadrotors aggressive flight, that takes
into account feature matchability during trajectory planning. We seek to
generate a perception-aware trajectory that reduces the error of visual-based
estimator while satisfying the constraints on smoothness, safety, agility and
the quadrotor dynamics. The perception objective is achieved by maximizing the
number of covisible features while ensuring small enough parallax angles.
Additionally, we propose a differentiable and accurate visibility model that
allows decomposition of the trajectory planning problem for efficient
optimization resolution. Through validations conducted in both a photorealistic
simulator and real-world experiments, we demonstrate that the trajectories
generated by our method significantly improve state estimation accuracy, with
root mean square error (RMSE) reduced by up to an order of magnitude. The
source code will be released to benefit the community.
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