6,139 research outputs found
Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-opera- tive morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilites by observ- ing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted in- struments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This paper reviews the state-of-the-art methods for optical intra-operative 3D reconstruction in laparoscopic surgery and discusses the technical challenges and future perspectives towards clinical translation. With the recent paradigm shift of surgical practice towards MIS and new developments in 3D opti- cal imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions
Self-Calibration of Cameras with Euclidean Image Plane in Case of Two Views and Known Relative Rotation Angle
The internal calibration of a pinhole camera is given by five parameters that
are combined into an upper-triangular calibration matrix. If the
skew parameter is zero and the aspect ratio is equal to one, then the camera is
said to have Euclidean image plane. In this paper, we propose a non-iterative
self-calibration algorithm for a camera with Euclidean image plane in case the
remaining three internal parameters --- the focal length and the principal
point coordinates --- are fixed but unknown. The algorithm requires a set of point correspondences in two views and also the measured relative
rotation angle between the views. We show that the problem generically has six
solutions (including complex ones).
The algorithm has been implemented and tested both on synthetic data and on
publicly available real dataset. The experiments demonstrate that the method is
correct, numerically stable and robust.Comment: 13 pages, 7 eps-figure
Joint Intrinsic and Extrinsic LiDAR-Camera Calibration in Targetless Environments Using Plane-Constrained Bundle Adjustment
This paper introduces a novel targetless method for joint intrinsic and
extrinsic calibration of LiDAR-camera systems using plane-constrained bundle
adjustment (BA). Our method leverages LiDAR point cloud measurements from
planes in the scene, alongside visual points derived from those planes. The
core novelty of our method lies in the integration of visual BA with the
registration between visual points and LiDAR point cloud planes, which is
formulated as a unified optimization problem. This formulation achieves
concurrent intrinsic and extrinsic calibration, while also imparting depth
constraints to the visual points to enhance the accuracy of intrinsic
calibration. Experiments are conducted on both public data sequences and
self-collected dataset. The results showcase that our approach not only
surpasses other state-of-the-art (SOTA) methods but also maintains remarkable
calibration accuracy even within challenging environments. For the benefits of
the robotics community, we have open sourced our codes
Medical SLAM in an autonomous robotic system
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects
Medical SLAM in an autonomous robotic system
One of the main challenges for computer-assisted surgery (CAS) is to determine the intra-operative morphology and motion of soft-tissues. This information is prerequisite to the registration of multi-modal patient-specific data for enhancing the surgeon’s navigation capabilities by observing beyond exposed tissue surfaces and for providing intelligent control of robotic-assisted instruments. In minimally invasive surgery (MIS), optical techniques are an increasingly attractive approach for in vivo 3D reconstruction of the soft-tissue surface geometry. This thesis addresses the ambitious goal of achieving surgical autonomy, through the study of the anatomical environment by Initially studying the technology present and what is needed to analyze the scene: vision sensors. A novel endoscope for autonomous surgical task execution is presented in the first part of this thesis. Which combines a standard stereo camera with a depth sensor. This solution introduces several key advantages, such as the possibility of reconstructing the 3D at a greater distance than traditional endoscopes. Then the problem of hand-eye calibration is tackled, which unites the vision system and the robot in a single reference system. Increasing the accuracy in the surgical work plan. In the second part of the thesis the problem of the 3D reconstruction and the algorithms currently in use were addressed. In MIS, simultaneous localization and mapping (SLAM) can be used to localize the pose of the endoscopic camera and build ta 3D model of the tissue surface. Another key element for MIS is to have real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy. Starting from the ORB-SLAM algorithm we have modified the architecture to make it usable in an anatomical environment by adding the registration of the pre-operative information of the intervention to the map obtained from the SLAM. Once it has been proven that the slam algorithm is usable in an anatomical environment, it has been improved by adding semantic segmentation to be able to distinguish dynamic features from static ones. All the results in this thesis are validated on training setups, which mimics some of the challenges of real surgery and on setups that simulate the human body within Autonomous Robotic Surgery (ARS) and Smart Autonomous Robotic Assistant Surgeon (SARAS) projects
Real-time Prostate Motion Tracking For Robot-assisted Laparoscopic Radical Prostatectomy
Radical prostatectomy surgery (RP) is the gold standard for treatment of localized prostate cancer (PCa). Recently, emergence of minimally invasive techniques such as Laparoscopic Radical Prostatectomy (LRP) and Robot-Assisted Laparoscopic Radical Prostatectomy (RARP) has improved the outcomes for prostatectomy. However, it remains difficult for surgeons to make informed decisions regarding resection margins and nerve sparing since the location of the tumour within the organ is not usually visible in a laparoscopic view. While MRI enables visualization of the salient structures and cancer foci, its efficacy in LRP is reduced unless it is fused into a stereoscopic view such that homologous structures overlap. Registration of the MRI image and peri-operative ultrasound image either via visual manual alignment or using a fully automated registration can potentially be exploited to bring the pre-operative information into alignment with the patient coordinate system at the beginning of the procedure. While doing so, prostate motion needs to be compensated in real-time to synchronize the stereoscopic view with the pre-operative MRI during the prostatectomy procedure. In this thesis, two tracking methods are proposed to assess prostate rigid rotation and translation for the prostatectomy. The first method presents a 2D-to-3D point-to-line registration algorithm to measure prostate motion and translation with respect to an initial 3D TRUS image. The second method investigates a point-based stereoscopic tracking technique to compensate for rigid prostate motion so that the same motion can be applied to the pre-operative images
Micro Fourier Transform Profilometry (FTP): 3D shape measurement at 10,000 frames per second
Recent advances in imaging sensors and digital light projection technology
have facilitated a rapid progress in 3D optical sensing, enabling 3D surfaces
of complex-shaped objects to be captured with improved resolution and accuracy.
However, due to the large number of projection patterns required for phase
recovery and disambiguation, the maximum fame rates of current 3D shape
measurement techniques are still limited to the range of hundreds of frames per
second (fps). Here, we demonstrate a new 3D dynamic imaging technique, Micro
Fourier Transform Profilometry (FTP), which can capture 3D surfaces of
transient events at up to 10,000 fps based on our newly developed high-speed
fringe projection system. Compared with existing techniques, FTP has the
prominent advantage of recovering an accurate, unambiguous, and dense 3D point
cloud with only two projected patterns. Furthermore, the phase information is
encoded within a single high-frequency fringe image, thereby allowing
motion-artifact-free reconstruction of transient events with temporal
resolution of 50 microseconds. To show FTP's broad utility, we use it to
reconstruct 3D videos of 4 transient scenes: vibrating cantilevers, rotating
fan blades, bullet fired from a toy gun, and balloon's explosion triggered by a
flying dart, which were previously difficult or even unable to be captured with
conventional approaches.Comment: This manuscript was originally submitted on 30th January 1
RCM-SLAM: Visual localisation and mapping under remote centre of motion constraints
In robotic surgery the motion of instruments
and the laparoscopic camera is constrained by their insertion
ports, i. e. a remote centre of motion (RCM). We propose a
Simultaneous Localisation and Mapping (SLAM) approach that
estimates laparoscopic camera motion under RCM constraints.
To achieve this we derive a minimal solver for the absolute
camera pose given two 2D-3D point correspondences (RCMPnP) and also a bundle adjustment optimiser that refines
camera poses within an RCM-constrained parameterisation.
These two methods are used together with previous work on
relative pose estimation under RCM [1] to assemble a SLAM
pipeline suitable for robotic surgery. Our simulations show that
RCM-PnP outperforms conventional PnP for a wide noise range
in the RCM position. Results with video footage from a robotic
prostatectomy show that RCM constraints significantly improve
camera pose estimatio
RCM-SLAM: Visual localisation and mapping under remote centre of motion constraints
In robotic surgery the motion of instruments and the laparoscopic camera is constrained by their insertion ports, i. e. a remote centre of motion (RCM). We propose a Simultaneous Localisation and Mapping (SLAM) approach that estimates laparoscopic camera motion under RCM constraints. To achieve this we derive a minimal solver for the absolute camera pose given two 2D-3D point correspondences (RCM-PnP) and also a bundle adjustment optimiser that refines camera poses within an RCM-constrained parameterisation. These two methods are used together with previous work on relative pose estimation under RCM [1] to assemble a SLAM pipeline suitable for robotic surgery. Our simulations show that RCM-PnP outperforms conventional PnP for a wide noise range in the RCM position. Results with video footage from a robotic prostatectomy show that RCM constraints significantly improve camera pose estimation
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