降低无人机户外监视中RGB与热成像数据的目标检测不确定性
Reducing Object Detection Uncertainty from RGB and Thermal Data for UAV Outdoor Surveillance
AI总结：
针对无人机户外监视中视觉检测存在不确定性的问题，提出基于POMDP、用ABT和TAPIR工具包机载实时求解的概率运动规划框架，经真实飞行测试验证可减少误报、提升遇难者定位精度。
摘要：
无人机（UAV）技术的最新进展使其迅速应用于广泛的民用领域，包括精准农业、生物安全、灾害监测与监视。无人机提供了硬件配置灵活的低成本平台，且自主能力不断增强，涵盖起飞、降落、目标跟踪和避障功能。然而，很少有研究关注无人机如何应对由视觉检测器误读、数据噪声、振动和遮挡导致的目标检测不确定性。在大多数情况下，由于许多无人机自主与环境交互的认知能力有限，这些检测结果的相关性与解读工作都交由人类操作员完成。本文提出了一种面向小型无人机户外场景的不确定性下自主导航框架，该框架采用基于概率的运动规划器。研究使用一架重量低于2千克的四旋翼无人机开展真实飞行测试对框架进行评估，并在森林/灌木地带的搜救（SAR）遇难者搜寻案例研究中进行了演示。导航问题采用部分可观测马尔可夫决策过程（POMDP）建模，并通过增强信念树（ABT）与TAPIR工具包在小型无人机机载端实时求解。使用彩色与热成像图像的实验结果表明，与基线运动规划器相比，所提出的运动规划器能提供准确的遇难者定位坐标，因为无人机可以灵活地与环境交互，获取任何潜在遇难者的更清晰图像。集成该系统可减少视觉目标检测器的误报读数，从而优化无人机监视作业。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
