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.

翻译：各类感知感知规划方法试图提升机动过程中的状态估计精度，而帧间特征可匹配性——这一影响估计精度的关键因素，常被忽视。本文提出APACE（Agile and Perception-Aware trajeCtory gEneration），一种针对四旋翼激进飞行的敏捷与感知感知轨迹生成框架，在轨迹规划中考虑了特征可匹配性。我们力求生成一条感知感知轨迹，在满足平滑性、安全性、敏捷性及四旋翼动力学约束的同时，降低基于视觉的估计器误差。该感知目标通过最大化共视特征数量并确保足够小的视差角来实现。此外，我们提出一种可微分且精确的可见性模型，支持将轨迹规划问题分解以实现高效优化求解。在逼真模拟器和真实世界实验中的验证表明，本方法生成的轨迹显著提升了状态估计精度，均方根误差（RMSE）最高可降低一个数量级。源代码将公开发布以惠及社区。