- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Context-Aware Depth and Pose Estimation for Bronchoscopic Navigation
摘要： Endobronchial intervention is increasingly used as a minimally invasive means of lung intervention. Vision-based localization approaches are often sensitive to image artifacts in bronchoscopic videos. In this paper, a robust navigation system based on a context-aware depth recovery approach for monocular video images is presented. To handle the artifacts, a conditional generative adversarial learning framework is proposed for reliable depth recovery. The accuracy of depth estimation and camera localization is validated on an in vivo dataset. Both quantitative and qualitative results demonstrate that the depth recovered with the proposed method preserves better structural information of airway lumens in the presence of image artifacts, and the improved camera localization accuracy demonstrates its clinical potential for bronchoscopic navigation.
关键词： Computer Vision for Medical Robotics,Deep Learning in Robotics and Automation,Visual-Based Navigation,Visual Learning
更新于2025-09-23 15:22:29
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Iteratively Reweighted Midpoint Method for Fast Multiple View Triangulation
摘要： The classic midpoint method for triangulation is extremely fast, but usually labelled as inaccurate. We investigate the cost function that the midpoint method tries to minimize, and the result shows that the midpoint method is prone to underestimate the accuracy of the measurement acquired relatively far from the 3D point. Accordingly, the cost function used in this work is enhanced by assigning a weight to each measurement, which is inversely proportional to the distance between the 3D point and the corresponding camera center. After analyzing the gradient of the modified cost function, we propose to do minimization by applying fixed-point iterations to find the roots of the gradient. Thus the proposed method is called the iteratively reweighted midpoint method. In addition, a theoretical study is presented to reveal that the proposed method is an approximation to the Newton's method near the optimal point, and hence inherits the quadratic convergence rate. At last, the comparisons of the experimental results on both synthetic and real datasets demonstrate that the proposed method is more efficient than the state-of-the-art while achieves the same level of accuracy.
关键词： SLAM,Visual-Based Navigation,Localization,Mapping
更新于2025-09-23 15:22:29
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Distinguishing Refracted Features using Light Field Cameras with Application to Structure from Motion
摘要： To be effective, robots will need to reliably operate in scenes with refractive objects in a variety of applications; however, refractive objects can cause many robotic vision algorithms, such as structure from motion, to become unreliable or even fail. We propose a novel method to distinguish between refracted and Lambertian image features using a light field camera. While previous refracted feature detection methods are limited to light field cameras with large baselines relative to the refractive object, our method achieves comparable performance, and we extend these capabilities to light field cameras with much smaller baselines than previously considered, where we achieve up to 50% higher refracted feature detection rates. Specifically, we propose to use textural cross correlation to characterize apparent feature motion in a single light field, and compare this motion to its Lambertian equivalent based on 4-D light field geometry. For structure from motion, we demonstrate that rejecting refracted features using our distinguisher yields lower reprojection error, lower failure rates, and more accurate pose estimates when the robot is approaching refractive objects. Our method is a critical step toward allowing robots to operate in the presence of refractive objects.
关键词： Computer vision for automation,light fields,computational imaging,visual-based navigation
更新于2025-09-04 15:30:14