作为在空中抛掷四旋翼飞行器后恢复的第一步，它需要检测它使用其加速度计的发射。理想的情况下，在飞行中，加速度计理想地仅测量由于施加的转子推力引起的加速度，即。因此，当四旋翼飞行器发射时，我们可以检测到测量的加速度下降到与当前施加的推力相对应的值。
B. Recovery and Initialization Steps
张宁 Perception-aware Receding Horizon Navigation for MAVs
"链接：https://pan.baidu.com/s/1uBMIFMFudZ6FXs4lSnUOLw
提取码：7br1"
To reach a given destination safely and accurately,a micro aerial vehicle needs to be able to avoid obstaclesand minimize its state estimation uncertainty at the sametime. To achieve this goal, we propose a perception-awarereceding horizon approach. In our method, a single forward-looking camera is used for state estimation and mapping.Using the information from the monocular state estimation andmapping system, we generate a library of candidate trajectoriesand evaluate them in terms of perception quality, collisionprobability, and distance to the goal. The best trajectory toexecute is then selected as the one that maximizes a reward function based on these three metrics. To the best of our knowledge, this is the first work that integrates active vision within a receding horizon navigation framework for a goal reaching task. We demonstrate by simulation and real-world experiments on an actual quadrotor that our active approach leads to improved state estimation accuracy in a goal-reaching task when compared to a purely-reactive navigation system,especially in difficult scenes (e.g., weak texture).
为了安全准确地到达给定目的地，微型飞行器需要能够避开障碍物并同时最小化其状态估计不确定性。为了实现这一目标，我们提出了一种感知感知的后退视界方法。 在我们的方法中，单个前视摄像机用于状态估计和映射。使用来自单眼状态估计和映射系统的信息，我们生成候选轨迹库并根据感知质量，碰撞概率和到目标的距离来评估它们。然后选择最佳执行轨迹作为基于这三个度量最大化奖励函数的轨迹。 就我们所知，这是第一项将主动视觉与后退地平线导航框架相结合以实现目标任务的工作。我们通过仿真和现实世界实验证明，与纯反应式导航系统相比，我们的主动方法可以在达到目标的任务中提高状态估计精度，尤其是在困难场景（例如，弱纹理）中。