Assessment and Improvement of Distance Measurement Accuracy for Time-of-Flight Cameras

Abstract

Time-of-flight depth cameras are interesting sensors for contact-less 3D metrology because they combine mechanical robustness with independence of ambient lighting conditions. Their actual performance depends on many factors and is hard to predict from data sheets. In this study we investigate the deviations of the distance measurements of a high-end phasebased depth camera. We focus on the impact of (i) self-warming and external temperature, (ii) on range noise as a function of distance and acquisition time, and (iii) on distance-dependent biases. We present the dedicated experimental setups comprising a climate chamber, a calibration bench with a reference interferometer, and a laser tracker that provide controlled conditions and ground truth data. These setups allow investigating the absolute accuracy and mitigating repeatable distance biases by adapting the measurement model based on experimental data. For demonstration, we apply the investigation to two stateof-the-art industrial depth cameras of the same brand and type (Helios Lucid), showing significantly different response to external temperature but similar distance-dependent biases. We adapt the measurement model of one of the cameras for distancedependent inter-pixel biases and demonstrate that the resulting parameters reduce also the distance biases of the other camera by about 80 % to less than 1 mm at ranges of up to 1 m. This indicates the potential for batch error compensation. The paper contributes to better understanding distance deviations of depth cameras and to improving the accuracy of such cameras.

Geometric Feedback System for Robotic Spraying

“…Further aspects to consider when performing depth camera measurements in industrial environments are predominantly related to the external temperature and its fluctuations. The outcomes of the research in Frangez et al (2022) indicate that when external temperature fluctuations are within 2 • C, all the depth measurement errors are expected to stay within 1 mm. The temperature effect on the measurements is predominantly related to the induced mechanical and electrical variations in the camera sensor.…”

“…For this, we use an intensity calibration function which is determined in an independent experiment. Additionally, the depth images are corrected for all the distance-related systematics and inter-pixel related-errors, by applying an estimated error compensation function (Frangez et al 2022). To avoid the loss of information by smoothing out the smallest surface features the images are not low-pass filtered or manipulated in any other way.…”

Miniature Mobile Robot Detection Using an Ultralow-Resolution Time-of-Flight Sensor

“…Robot systems for navigation most commonly use visual, sound, and electromagnetic sensors. The most used technologies are 2D and 3D LIDAR [1], visible spectrum [2], infrared spectrum [3], and Time-of-Flight (ToF) cameras [4], and ultrasonic sensors [5]. Low-cost miniature ToF sensors are becoming increasingly popular in miniature mobile robotics in combination with microcontrollers [6]- [8].…”

LFRSNet: A robust light field semantic segmentation network combining contextual and geometric features

“…Semantic segmentation, which assigns semantic labels for each pixel in an image, has drawn great attention in recent years. With high-level understanding of images, it facilitates many smart city applications like remote sensing (Li F et al, 2022;Li Y et al, 2022), object tracking (Zhang et al, 2020;, virtual reality (Gao et al, 2022;Gu et al, 2022) and 3D photogrammetry (Frangez et al, 2022;Wang H et al, 2022). Based on fully convolutional networks, semantic segmentation is successfully conducted with single images, videos and RGB-D data considering different application scenarios.…”