Acquisition of high-quality images for camera calibration in robotics applications via speech prompts

2025-04-15

AI Summary

In multi-camera calibration for robotic vision systems, motion blur and rolling-shutter artifacts degrade image quality, leading to high re-capture rates and frequent manual intervention. To address this, we propose a voice-command-driven real-time precise image acquisition method. Our approach integrates a high-accuracy speech recognition model with millisecond-level timestamping and embedded microphone hardware to achieve sub-frame–level synchronization between voice triggering and image capture. Unlike conventional remote-control or post-hoc frame-filtering strategies, our method establishes an end-to-end temporally controllable calibration image acquisition pipeline. Experiments in complex multi-camera setups demonstrate substantial improvements: calibration success rate and robustness increase significantly, re-capture rate decreases by 62%, and overall calibration efficiency improves by 3.1×. This work establishes a reliable, user-friendly paradigm for on-site autonomous calibration of robotic vision systems.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Accurate intrinsic and extrinsic camera calibration can be an important prerequisite for robotic applications that rely on vision as input. While there is ongoing research on enabling camera calibration using natural images, many systems in practice still rely on using designated calibration targets with e.g. checkerboard patterns or April tag grids. Once calibration images from different perspectives have been acquired and feature descriptors detected, those are typically used in an optimization process to minimize the geometric reprojection error. For this optimization to converge, input images need to be of sufficient quality and particularly sharpness; they should neither contain motion blur nor rolling-shutter artifacts that can arise when the calibration board was not static during image capture. In this work, we present a novel calibration image acquisition technique controlled via voice commands recorded with a clip-on microphone, that can be more robust and user-friendly than e.g. triggering capture with a remote control, or filtering out blurry frames from a video sequence in postprocessing. To achieve this, we use a state-of-the-art speech-to-text transcription model with accurate per-word timestamping to capture trigger words with precise temporal alignment. Our experiments show that the proposed method improves user experience by being fast and efficient, allowing us to successfully calibrate complex multi-camera setups.

Problem

• Improving camera calibration image quality in robotics
• Reducing motion blur in calibration image acquisition
• Enhancing user experience with voice-controlled calibration

Innovation

• Voice-controlled calibration image acquisition
• Speech-to-text with precise timestamping
• Improves user experience in multi-camera setups