Single-Shot Rectified Stereo Baseline Refinement for Industrial Robotics

Abstract

In the dynamic realm of industrial robotics, prolonged operation amidst vibrations and occasional collisions presents formidable challenges to sensor calibration. Fast and effective methodologies for assessing and remedying calibration discrepancies are imperative to mitigate these issues. In response, we introduce an approach in target-based stereo baseline calibration refinement tailored for real-world industrial environments. Our method offers a means of incrementally assessing accuracy and correcting baseline drift in rectified stereo images. Departing from conventional approaches, our calibration methodology operates under the assumption of an initial stereo camera calibration estimate, focusing exclusively on adjusting minor baseline variations. This streamlined process facilitates efficient enhancement of depth precision, circumventing the need for cumbersome calibration routines. Furthermore, we design a calibration target specifically for this process, combining ArUco markers for precise visual detection with a pseudorandom pattern for robust depth estimation. Through extensive experimentation, we demonstrate the superiority of our method over traditional stereo camera calibration techniques in optimizing depth accuracy. Overall, our work presents a practical solution for maintaining high-fidelity depth estimation in long-lived stereo vision systems deployed within industrial settings, underscoring its relevance and applicability in real-world scenarios.