Noise Analysis and Modeling of the PMD Flexx2 Depth Camera for Robotic Applications

Ahstract-Time of Flight (ToF) cameras, renowned for their ability to capture real-time 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 stereo-based 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 non-systematic 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 Kullback-Leibler (KL) divergence of 0.015 nats, reflecting precise noise characterization. Lateral noise, deviating from a Gaussian distribution, was modeled conservatively, yielding a satisfactory KL divergence of 0.868 nats. These results validate our noise models, crucial for accurately simulating sensor behavior in virtual environments and reducing the sim-to-real gap in learning-based control approaches.