Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras

Abstract

Knowledge about the interior and exterior camera orientation parameters is required to establish the relationship between 2D image content and 3D object data. Camera calibration is used to determine the interior orientation parameters, which are valid as long as the camera remains stable. However, information about the temporal stability of low-cost cameras due to the physical impact of temperature changes, such as those in smartphones, is still missing. This study investigates on the one hand the influence of heat dissipating smartphone components at the geometric integrity of implemented cameras and on the other hand the impact of ambient temperature changes at the geometry of uncoupled low-cost cameras considering a Raspberry Pi camera module that is exposed to controlled thermal radiation changes. If these impacts are neglected, transferring image measurements into object space will lead to wrong measurements due to high correlations between temperature and camera’s geometric stability. Monte-Carlo simulation is used to simulate temperature-related variations of the interior orientation parameters to assess the extent of potential errors in the 3D data ranging from a few millimetres up to five centimetres on a target in X- and Y-direction. The target is positioned at a distance of 10 m to the camera and the Z-axis is aligned with camera’s depth direction.

Multi-Epoch and Multi-Imagery (MEMI) Photogrammetric Workflow for Enhanced Change Detection Using Time-Lapse Cameras

“…Furthermore, it is important to retrieve suitable lens calibration parameters [39], which is especially the case for consumer grade cameras that usually exhibit stronger distortions. More recently, Elias et al [40] carried out a study to estimate the effect of temperature changes on low-cost camera sensors, highlighting the importance of also considering the temporal stability of cameras, especially during long-term observations.…”

“…Although the camera systems are fixed to the ground, camera movements are possible, e.g., due to temperature changes, wind or animals. Also, changes in the interior camera geometry are likely due to heating and cooling of the housing [40,59]. In this study we used the Lucas-Kanade method [60,61], also implemented in OpenCV, which has been shown to be suitable for tracking targets in geoscience applications [62].…”

On-Site Geometric Calibration of Thermal and Optical Sensors for Uas Photogrammetry

“…However, off-the-shelf sensors often have low geometric stability that can affect the calibration when transported, e.g. over rough tracks and due to deformation with changing temperature (Elias et al, 2020, SanzAblanedo et al, 2020. Cramer et al, (2017) investigated user grade UAS sensors in lab experiments and found large variations of geometric stability.…”

Automated calibration of smartphone cameras for 3D reconstruction of mechanical pipes

“…In fact, more inlier points suggest the existence of more correct point correspondences between different images, which consequentially provides a better estimation of the EOPs, especially with the correct camera model (see explanations given in the literature review around the essential matrix). The higher number of matched features could be a consequence of factors such as calibration precision (see Table I), type of video compression (for example, H.265 and H.264 codec for the iPhone 11 and Huawei P30, respectively) and higher relative stability of calibration parameters, a common dilemma in smartphone camera calibration (Chikatsu and Takahashi, 2009; Elias et al, 2020). The determination of the exact reasons for the disparity between different instruments would be an interesting avenue for future exploration.…”