Odometry consists in using data from a moving sensor to estimate change in position over time. It is a crucial step for several applications in robotics and computer vision. This paper presents a novel approach for estimating the relative motion between successive RGB-D frames that uses plane-primitives instead of point features. The planes in the scene are extracted and the motion estimation is cast as a plane-to-plane registration problem with a closed-form solution. Point features are only extracted in the cases where the plane surface configuration is insufficient to determine motion with no ambiguity. The initial estimate is refined in a photo-geometric optimization step that takes full advantage of the plane detection and simultaneous availability of depth and visual appearance cues. Extensive experiments show that our plane-based approach is as accurate as state-of-the-art point-based approaches when the camera displacement is small, and significantly outperforms them in case of wide-baseline and/or dynamic foreground.
Image keypoints are broadly used in robotics for different purposes, ranging from recognition to 3D reconstruction, passing by SLAM and visual servoing. Robust keypoint matching across different views is problematic because of the relative motion between camera and scene that causes significant changes in feature appearance. The problem can be partially overcome by using state-of-the-art methods for keypoint detection and matching, that are resilient to common affine transformations such as changes in scale and rotation. Unfortunately, these approaches are not invariant to the radial distortion present in images acquired by cameras with wide field-of-view. This article proposes modifications to the Scale Invariant Feature Transform (SIFT), that improve the repeatability of detection and effectiveness of matching in the presence of distortion, while preserving the characteristics of invariance to scale and rotation. These modifications require an approximate modeling of the image distortion, and consist in using adaptative gaussian filtering for detection and implicit gradient correction for description. Extensive experiments, with both synthetic and real images, show that our method outperforms explicit distortion correction using image rectification.
Abstract. Many image-based systems for aiding the surgeon during minimally invasive surgery require the endoscopic camera to be calibrated at all times. This article proposes a method for accomplishing this goal whenever the camera has optical zoom and the focal length changes during the procedure. Our solution for online calibration builds on recent developments in tracking salient points using differential image alignment, is well suited for continuous operation, and makes no assumptions about the camera motion or scene rigidity. Experimental validation using both a phantom model and in vivo data shows that the method enables accurate estimation of focal length when the zoom varies, avoiding the need to explicitly recalibrate during surgery. To the best of our knowledge this the first work proposing a practical solution for online zoom calibration in the operation room.
In the future, medicine will increasingly rely in Computer Aided Surgery and Diagnosis, hence techniques to extract information from endoscopic images of human-body cavities will be crucial. The ArthroNav project main goal is to process the endoscopic video in order to improve practioner's perception and navigation skill inside the knee joint.Some navigation systems for CAS (Computer Aided Surgery) based on image processing techniques have been already proposed in the literature. However, most of them employ methods designed for conventional cameras and do not account with the special optical arrangement of the endoscopic lens. We intend to adapt/design new computer vision techniques invariant to the numerous challenges that endoscopic video places.The usefulness of developing algorithms for endoscopic images is not limited to Computer Assisted Orthopedic Surgery. Many other medical fields can benefit from the research herein proposed. The processing of endoscopic video can be equally used in other minimally invasive procedures like nose surgery, neurosurgery, and cardiac surgery.
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