Sense and avoid based on visual pose estimation for small UAS
Sense and avoid based on visual pose estimation for small UAS
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DOI：
10.1109/iros.2017.8206188
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Changkoo Kang;Jason Davis;C. Woolsey;Seongim Choi
中科院分区：
文献类型：
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作者：
Changkoo Kang;Jason Davis;C. Woolsey;Seongim Choi
小型无人机系统（UAS）必须能够检测和避免冲突的交通，当威胁是另一个小型UAS时，这是一项特别具有挑战性的任务。避碰需要轨迹预测，通过扩展预测范围可以提高避碰系统的性能。我们描述了一种算法，预测的轨迹的一个小的，固定翼无人机使用其方向的估计。首先，计算机视觉算法在图像中定位威胁飞机的特定特征点。接下来，POSIT算法使用这些特征点来估计威胁的姿态（位置和姿态）。然后使用一系列姿态估计来预测威胁飞机的轨迹，以避免碰撞。为了评估该算法的性能，预测与预测的基础上，仅对各种遭遇的情况下的位置估计。仿真和实验结果表明，使用方向估计的轨迹预测提供了更快的响应威胁飞机轨迹的变化和更好的预测和回避性能。
Small unmanned aircraft systems (UAS) must be able to detect and avoid conflicting traffic, an especially challenging task when the threat is another small UAS. Collision avoidance requires trajectory prediction and the performance of a collision avoidance system can be improved by extending the prediction horizon. We describe an algorithm that predicts the trajectory of a small, fixed-wing UAS using an estimate of its orientation. First, a computer vision algorithm locates specific feature points of the threat aircraft in an image. Next, the POSIT algorithm uses these feature points to estimate the pose (position and attitude) of the threat. A sequence of pose estimates is then used to predict the trajectory of the threat aircraft in order to avoid a collision. To assess the algorithm's performance, the predictions are compared with predictions based solely on position estimates for a variety of encounter scenarios. Simulation and experimental results indicate that trajectory prediction using orientation estimates provides quicker response to a change in the threat aircraft trajectory and better prediction and avoidance performance.