Heredia Conde, Miguel
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Publication Access status: Metadata only , Kalman Filter-Driven Blind Source Localization for Passive 3D ToF Imaging(2023); ; Passive 3D Time-of-Flight (ToF) imaging faces a significant challenge in accurate depth recovery, where the source position is unavailable. In this letter, we present a probabilistic approach based on the Kalman filter which keeps track of source location, thus, avoiding the need for an initial guess to jointly determine the 3D source position and correct depth information. The proposed approach is able to reach a source location error of 0.8 cm by exploiting pseudo-measurements of the plane fitting constraint acquired by a passive ToF camera which exploits a bistatic algorithm with a gradient descent method. Computer experiments are carried out to demonstrate the robustness of the proposed method in realistic scenarios where no initial guess is available. The results show the feasibility of blind source localization with mm accuracy. This contributes to widening the applicability of passive 3D imaging in a number of application fields, such as autonomous driving, and robot navigation.Source Type:Article4 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , Performance Evaluation of State-of-the-Art High-Resolution Time-of-Flight CamerasIt has been about 20 years since the disruptive appearance of the first Time-of-Flight (ToF) cameras. Since then, ToF imaging has progressively evolved. Nowadays, ToF sensors have broken the barrier of the 1 MPix resolution and a significant number of high-resolution ToF cameras has appeared in the market. In order to provide a better understanding of their performance and applications, we experimentally evaluate three state-of-the-art high-resolution ToF cameras such as Azure Kinect, Helios2, and S100D, together with the solid-state LiDAR L515. We perform various experiments to examine some key parameters, such as warm-up times, accuracy, precision, lateral and axial resolutions, edge noise, unsteady scenes, and modulated waveform and optical power. Our evaluation draws various conclusions: S100D shows fluctuations within 1 mm after being powered up while the others require warm-up times. Azure Kinect, Helios2, and L515 can achieve precision within 2 mm in a measuring range of 0.5 m to 3 m. Helios2 and S100D are more severely affected by dynamic scenes. Finally, the point clouds generated for a white panel at a distance of 1.5 m show that flying pixels are present in all cameras, being this problem less acute for the L515.Source Type:Article5