Image-based registration for a neurosurgical robot: comparison using iterative closest point and coherent point drift algorithms

AbstractStereotactic neurosurgical robots allow quick, accurate location of small targets within the brain, relying on accurate registration of pre-operative MRI/CT images with patient and robot coordinate systems during surgery. Fiducial markers or a stereotactic frame are used as registration landmarks; the patient's head is fixed in position throughout surgery. An image-based system could be quicker and less invasive, allowing the head to be moved during surgery to give greater ease of access, but would be required to retain a surgical precision of ∼1mm at the target point.We compare two registration algorithms, iterative closest point (ICP) and coherent point drift (CPD), by registering ideal point clouds taken from MRI data with re-meshed, noisy and smoothed versions. We find that ICP generally gives better and more consistent registration accuracy for the region of interest than CPD, with a best RMS distance of 0.884±0.050mm between aligned point clouds, as compared to 0.995±0.170mm or worse for CPD

Underwater Cave Mapping and Reconstruction Using Stereo Vision

This work presents a systematic approach for 3-D mapping and reconstruction of underwater caves. Exploration of underwater caves is very important for furthering our understanding of hydrogeology, managing efficiently water resources, and advancing our knowledge in marine archaeology. Underwater cave exploration by human divers however, is a tedious, labor intensive, extremely dangerous operation, and requires highly skilled people. As such, it is an excellent fit for robotic technology. The proposed solution employs a stereo camera and a video-light. The approach utilizes the intersection of the cone of video-light with the cave boundaries resulting in the construction of a wire frame outline of the cave. Successive frames produce a scalable accurate point cloud which, through the use of adapted 3-D geometry reconstruction techniques, creates a fully replicated model of the cave system

Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits

We present a novel and effective method for detecting 3D primitives in cluttered, unorganized point clouds, without axillary segmentation or type specification. We consider the quadric surfaces for encapsulating the basic building blocks of our environments - planes, spheres, ellipsoids, cones or cylinders, in a unified fashion. Moreover, quadrics allow us to model higher degree of freedom shapes, such as hyperboloids or paraboloids that could be used in non-rigid settings. We begin by contributing two novel quadric fits targeting 3D point sets that are endowed with tangent space information. Based upon the idea of aligning the quadric gradients with the surface normals, our first formulation is exact and requires as low as four oriented points. The second fit approximates the first, and reduces the computational effort. We theoretically analyze these fits with rigor, and give algebraic and geometric arguments. Next, by re-parameterizing the solution, we devise a new local Hough voting scheme on the null-space coefficients that is combined with RANSAC, reducing the complexity from $O(N^4)$ to $O(N^3)$ (three points). To the best of our knowledge, this is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes without segmentation. Our extensive qualitative and quantitative results show that our method is efficient and flexible, as well as being accurate.Comment: Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI). arXiv admin note: substantial text overlap with arXiv:1803.0719