Recent advances in Unmanned Aerial Vehicles (UAVs) have resulted in their quick adoption for wide a range of civilian applications, including precision agriculture, biosecurity, disaster monitoring and surveillance. UAVs offer low-cost platforms with flexible hardware configurations, as well as an increasing number of autonomous capabilities, including take-off, landing, object tracking and obstacle avoidance. However, little attention has been paid to how UAVs deal with object detection uncertainties caused by false readings from vision-based detectors, data noise, vibrations, and occlusion. In most situations, the relevance and understanding of these detections are delegated to human operators, as many UAVs have limited cognition power to interact autonomously with the environment. This paper presents a framework for autonomous navigation under uncertainty in outdoor scenarios for small UAVs using a probabilistic-based motion planner. The framework is evaluated with real flight tests using a sub 2 kg quadrotor UAV and illustrated in victim finding Search and Rescue (SAR) case study in a forest/bushland. The navigation problem is modelled using a Partially Observable Markov Decision Process (POMDP), and solved in real time onboard the small UAV using Augmented Belief Trees (ABT) and the TAPIR toolkit. Results from experiments using colour and thermal imagery show that the proposed motion planner provides accurate victim localisation coordinates, as the UAV has the flexibility to interact with the environment and obtain clearer visualisations of any potential victims compared to the baseline motion planner. Incorporating this system allows optimised UAV surveillance operations by diminishing false positive readings from vision-based object detectors.

翻译：近年来，无人驾驶飞行器（UAV）的快速发展使其被迅速应用于广泛的民用领域，包括精准农业、生物安全、灾害监测与监控。无人机提供低成本平台，具有灵活的硬件配置以及日益增加的自主能力，包括起飞、降落、目标跟踪和避障。然而，针对无人机如何处理由视觉检测器产生的误报、数据噪声、振动和遮挡所导致的目标检测不确定性问题，目前关注较少。在大多数情况下，这些检测结果的相关性和理解被交由人类操作员处理，因为许多无人机的认知能力有限，无法自主与环境交互。本文提出一种框架，用于小型无人机在室外场景下基于概率运动规划器实现不确定性条件下的自主导航。该框架通过使用一架重量低于2公斤的四旋翼无人机进行实际飞行测试评估，并在森林/灌木丛中的遇难者搜索与救援（SAR）案例研究中加以说明。导航问题采用部分可观测马尔可夫决策过程（POMDP）建模，并在小型无人机上搭载增强信念树（ABT）和TAPIR工具包进行实时求解。使用彩色和热成像图像的实验结果表明，与基准运动规划器相比，所提出的运动规划器能够提供准确的遇难者定位坐标，因为无人机具有与环境交互的灵活性，并能获得任何潜在遇难者更清晰的视觉呈现。集成该系统可通过减少基于视觉目标检测器的误报读数来优化无人机监控操作。