Time of Flight ToF cameras renowned for their ability to capture realtime 3D information have become indispensable for agile mobile robotics These cameras utilize light signals to accurately measure distances enabling robots to navigate complex environments with precision Innovative depth cameras characterized by their compact size and lightweight design such as the recently released PMD Flexx2 are particularly suited for mobile robots Capable of achieving high frame rates while capturing depth information this innovative sensor is suitable for tasks such as robot navigation and terrain mapping Operating on the ToF measurement principle the sensor offers multiple benefits over classic stereobased depth cameras However the depth images produced by the camera are subject to noise from multiple sources complicating their simulation This paper proposes an accurate quantification and modeling of the nonsystematic noise of the PMD Flexx2 We propose models for both axial and lateral noise across various camera modes assuming Gaussian distributions Axial noise modeled as a function of distance and incidence angle demonstrated a low average KullbackLeibler KL divergence of 0015 nats reflecting precise noise characterization Lateral noise deviating from a Gaussian distribution was modeled conservatively yielding a satisfactory KL divergence of 0868 nats These results validate our noise models crucial for accurately simulating sensor behavior in virtual environments and reducing the simtoreal gap in learningbased control approaches

翻译：飞行时间（ToF）相机以其实时捕获三维信息的能力而闻名，已成为敏捷移动机器人不可或缺的组成部分。这类相机利用光信号精确测量距离，使机器人能够精准地在复杂环境中导航。创新的深度相机，如最近发布的PMD Flexx2，以其紧凑尺寸和轻量化设计为特点，特别适用于移动机器人。该创新传感器能够以高帧率捕获深度信息，适用于机器人导航和地形测绘等任务。基于ToF测量原理，该传感器相较于经典的基于立体的深度相机具有多重优势。然而，相机产生的深度图像受到多种来源的噪声影响，这使其仿真变得复杂。本文提出了对PMD Flexx2非系统性噪声的精确量化与建模。我们针对相机的不同模式，假设高斯分布，分别提出了轴向噪声和横向噪声的模型。轴向噪声建模为距离和入射角的函数，其平均Kullback-Leibler（KL）散度低至0.015 nats，反映了精确的噪声表征。横向噪声偏离高斯分布，我们对其进行了保守建模，获得了0.868 nats的满意KL散度。这些结果验证了我们的噪声模型，这对于在虚拟环境中精确模拟传感器行为以及缩小基于学习的控制方法中的仿真与现实差距至关重要。