500万像素立体视觉让机器人看得更远更准，关键靠精准快速的校准与匹配。
High-Definition 5MP Stereo Vision Sensing for Robotics
- 开发新帧间校准与立体匹配方法，兼顾高精度与实时性。
- 实测显示，500万像素相机仅在高精度校准下才能生成高质量点云。
- 提出用高算力算法生成真值图，真实评估实时性能。
高分辨率（500万像素以上）立体视觉系统对提升机器人能力至关重要，可实现更远距离作业并生成更密集、更精确的3D点云。然而，充分发挥高角分辨率传感器潜力需更高校准精度和更快处理速度，传统方法常无法满足。本研究通过新型帧间校准与立体匹配方法，处理500万像素图像，在保证高精度的同时实现高速处理。此外，提出一种新评估方式：将实时生成的视差图与基于高计算量算法生成的真值视差图对比，以真实反映实时性能。研究关键表明，只有在高精度校准下，高像素相机才能产出高质量点云。
原文摘要 · Abstract (English)
High-resolution (5MP+) stereo vision systems are essential for advancing robotic capabilities, enabling operation over longer ranges and generating significantly denser and accurate 3D point clouds. However, realizing the full potential of high-angular-resolution sensors requires a commensurately higher level of calibration accuracy and faster processing -- requirements often unmet by conventional methods. This study addresses that critical gap by processing 5MP camera imagery using a novel, advanced frame-to-frame calibration and stereo matching methodology designed to achieve both high accuracy and speed. Furthermore, we introduce a new approach to evaluate real-time performance by comparing real-time disparity maps with ground-truth disparity maps derived from more computationally intensive stereo matching algorithms. Crucially, the research demonstrates that high-pixel-count cameras yield high-quality point clouds only through the implementation of high-accuracy calibration.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。