Autonomous Aerial Vehicle Navigation without Reliance on GPS

不依赖 GPS 的自主飞行器导航

• 批准号: RGPIN-2014-03915
• 负责人: Waslander, Steven
• 金额: $ 1.75万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=668228
• 关键词: Autonomous; Aerial; Vehicle; Navigation; without

Small autonomous rotorcraft have seen a surge of activity over the last decade, and have demonstrated some fascinating indoor capabilities such as autonomous flight through windows, synchronized flying, and even playing catch. However, progress seems stalled at this point, as systems are currently required to operate within an indoor positioning system (IPS), or in the open where GPS satellite visibility can be ensured. Small rotorcraft are ideally suited to inspection tasks that require precise positioning in close proximity to objects, but their reliance on IPS or GPS for accurate localization and their inability to perceive their environment and navigate through it autonomously drastically reduces their current utility. As a result, today's small UAVs are essentially tethered to within line-of-sight of operators and away from any objects in their surroundings.**It is time to cut the tether. By augmenting quadrotor vehicles with multi-camera clusters, I plan to develop vision-based localization and mapping algorithms that can complement GPS information and detect obstacles in the environment. Vision-based localization can act as an estimation refinement for GPS and inertial measurements or independently if necessary. Multiple cameras can be easily integrated into small rotorcraft and will provide robust motion estimation and reasonable 3D scene reconstruction, as long as sufficient computation power is available. **The approach to full quadrotor autonomy outlined in this proposal involves four main components. The first component is to develop new low-power, light weight vision and computing modules that exploit the latest advances from smartphone chips to offload much of the computation effort from the central unit. The high framerates that can be achieved with this architecture will be the first defense against poor feature correspondence that plagues current approaches.**The next component involves developing robust methods for vehicle localization in static environments. Given known sensor models and accurate motion predictions, it will be possible to apply integrity monitoring techniques such as parity space thresholding to not only reject erroneous measurements, but do so knowing the probability of failure and false positives when designing the system. The challenge lies in developing the necessary fundamental changes to the description of the visual sensor data, and the expansion of parity space integrity monitoring techniques to nonlinear systems with large numbers of measurements and multiple outliers.**The third component involves the development of a dense reconstruction of the scene for motion planning purposes. Relying on occupancy grid map representations that permit rapid assimilation of new measurements, an overapproximation to the environment will be constructed based on all tracked features in the environment. **Finally, the above methods rely on a static environment assumption, but most applications require autonomous operation near moving objects such as people and vehicles. We will develop methods to detect and track moving objects, eliminating their current negative effect on motion estimation, and enabling vision based tracking and collision avoidance.**With these components integrated onto a single quadrotor vehicle, it will finally be possible to operate autonomously in close proximity to obstacles with and without reliable GPS measurements. Vast inspection markets in manufacturing, energy generation and distribution, infrastructure maintenance and videography await once rotorcraft are able to assess dangers and fly with guaranteed reliability regardless of their surroundings.

在过去的十年里，小型自主旋翼机的活动激增，并展示了一些迷人的室内功能，如通过窗户的自主飞行，同步飞行，甚至玩接球。 然而，进展似乎在这一点上停滞不前，因为系统目前需要在室内定位系统（IPS）内运行，或者在可以确保GPS卫星可见性的开放环境中运行。 小型旋翼机非常适合需要在物体附近精确定位的检查任务，但它们依赖IPS或GPS进行精确定位，无法感知环境并自主导航，这大大降低了它们目前的实用性。 因此，今天的小型无人机基本上都被拴在操作员的视线范围内，远离周围的任何物体。**是时候切断绳索了。 通过用多摄像机集群增强四旋翼飞行器，我计划开发基于视觉的定位和映射算法，可以补充GPS信息并检测环境中的障碍物。 基于视觉的定位可以作为GPS和惯性测量的估计细化，或者在必要时独立地进行。 多个摄像机可以很容易地集成到小型旋翼机中，只要有足够的计算能力，就可以提供鲁棒的运动估计和合理的3D场景重建。** 本提案中概述的完全四旋翼自主的方法包括四个主要组成部分。 第一个组成部分是开发新的低功耗，重量轻的视觉和计算模块，利用智能手机芯片的最新进展，从中央单元中卸载大部分计算工作。 这种架构可以实现的高帧率将是对困扰当前方法的不良特征对应的第一道防线。**下一个组成部分涉及开发静态环境中车辆定位的鲁棒方法。 给定已知的传感器模型和准确的运动预测，将有可能应用诸如奇偶空间阈值化的完整性监测技术，以不仅拒绝错误的测量，而且在设计系统时知道故障和误报的概率。挑战在于对视觉传感器数据的描述进行必要的根本性改变，以及将奇偶空间完整性监测技术扩展到具有大量测量和多个异常值的非线性系统。第三个组成部分涉及到一个密集的重建场景的运动规划的目的。 依靠允许快速同化新测量值的占用网格地图表示，将根据环境中所有跟踪的特征构建对环境的过度逼近。 ** 最后，上述方法依赖于静态环境假设，但大多数应用需要在移动物体（如人和车辆）附近进行自主操作。 我们将开发检测和跟踪移动物体的方法，消除它们对运动估计的当前负面影响，并实现基于视觉的跟踪和碰撞避免。通过将这些组件集成到单个四旋翼飞行器上，最终将有可能在有或没有可靠的GPS测量的情况下在靠近障碍物的地方自主运行。 一旦旋翼机能够评估危险并在任何环境下都能可靠地飞行，制造业、能源生产和分配、基础设施维护和视频拍摄等领域的巨大检测市场就在等待着它。