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PERFORMANCE ANALYSIS OF UAV VISUAL LANDMARK TRACKING UNDER RAPID MOTION

机译:快速运动下的无人机视觉地标跟踪性能分析

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With reduced drone cost, object tracking and localization algorithms are well studied for robotics research. In most cases, the scene is stationary and/or motion is smooth so that control system reference input varies less frequently: only at the sharp edges generated by path planning algorithm. However, using systems such as co-robots among human beings, in windy situations and/or around more sophisticated structures will require rapid localization, object detection and obstacle avoidance solutions due to continuously varying controller input and path plan. This research effort benchmarks fundamental object detecting and tracking techniques under highly dynamic disturbances. To perform these tests an experimental system was developed that could create rapid angular motion in a controlled manner. A sensor suite of a visual sensor and an IMU is assembled into a rig. It is mounted on tip of a 6-DOF multi-joint mechanism which consists of a 5-DOF robotic arm and a pendulum mounted to the end effector. While robotic arm is programmed to simulate aggressive drone motion (max. 60 degrees/sc), pendulum generates an additional highly dynamic angular motion about another plane. This motion can be used to benchmark various types of tracking algorithms, allowing for a comparison in robustness with regards to different motions. The specific motion that will be explored in this research is rapid angular motion. A feature rich landmark is used to execute the experiments. Velocity, acceleration and viewing angle are varied continuously to benchmark basic search-based detection methods using gradient magnitude which is the fundamental step for advanced algorithms. Finally, results with different algorithm parameters are listed to compare robustness of the solutions. Overall system repeatability and precision are discussed with various plots of 2700 processed images.
机译:随着无人机成本的降低,对对象跟踪和定位算法进行了很好的研究,以进行机器人研究。在大多数情况下,场景是静止的和/或运动是平滑的,因此控制系统参考输入的更改频率较低:仅在路径规划算法生成的尖锐边缘处。然而,由于连续变化的控制器输入和路径计划,在大风中和/或在更复杂的结构周围使用诸如人类之间的协同机器人之类的系统将需要快速的定位,目标检测和避障解决方案。这项研究工作对在高度动态干扰下的基本目标检测和跟踪技术进行了基准测试。为了进行这些测试,开发了一种实验系统,该系统可以以受控方式产生快速角运动。视觉传感器和IMU的传感器套件被组装到装备中。它安装在6自由度多关节机构的末端,该机构由5自由度机械臂和安装在末端执行器上的摆锤组成。当对机械手进行编程以模拟激进的无人机运动(最大60度/ sc)时,摆锤会围绕另一个平面产生附加的高动态角运动。该运动可用于对各种类型的跟踪算法进行基准测试,从而可以针对不同运动对鲁棒性进行比较。在这项研究中将要探讨的特定运动是快速角运动。使用功能丰富的地标来执行实验。速度,加速度和视角不断变化,以使用梯度幅度作为基于基本搜索的检测方法的基准,这是高级算法的基本步骤。最后,列出了具有不同算法参数的结果,以比较解决方案的鲁棒性。讨论了整个系统的可重复性和精度,并绘制了2700张处理过的图像的各个图。

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