Dense-ArthroSLAM: Dense Intra-Articular 3-D Reconstruction With Robust Localization Prior for Arthroscopy

Marmol, Andres; Banach, Artur; Peynot, Thierry

doi:10.1109/lra.2019.2892199

Cited by 33 publications

(18 citation statements)

References 16 publications

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“…[20] proposes an EKF-SLAM algorithm, and [25] gets dense maps based on [29]. [26] uses a rigid SLAM system to locate the camera in arthroscopic images. All of these methods assume that the deformation is negligible and hence that a purely rigid SLAM system is able to survive just by excluding from the map any deformed scene region.…”

Section: Rigid Visual Slammentioning

confidence: 99%

DefSLAM: Tracking and Mapping of Deforming Scenes From Monocular Sequences

et al. 2021

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We present the first monocular SLAM capable of operating in deforming scenes in real-time. Our DefSLAM approach fuses isometric Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) techniques to deal with the exploratory sequences typical of SLAM. A deformation tracking thread recovers the pose of the camera and the deformation of the observed map at frame rate by means of SfT. A deformation mapping thread runs in parallel to update the template at keyframe rate by means of NRSfM with a batch of covisible keyframes. In our experiments, DefSLAM processes sequences of deforming scenes both in a laboratory controlled experiment and in medical endoscopy sequences, being able to produce accurate 3D models of the scene with respect to the moving camera.

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Section: Rigid Visual Slammentioning

confidence: 99%

DefSLAM: Tracking and Mapping of Deforming Scenes From Monocular Sequences

et al. 2021

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“…Intuitively, LM algorithm tries to find a balance between Gaussian-Newton method and the gradient descent solver. In our implementation, (19) is solved with a GPU version of the preconditioned conjugate gradient method within 10 iterations.…”

Section: Deformable Tissue Trackingmentioning

confidence: 99%

SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation With Surgical Robotics

Richter

et al. 2020

IEEE Robot. Autom. Lett.

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Traditional control and task automation have been successfully demonstrated in a variety of structured, controlled environments through the use of highly specialized modelled robotic systems in conjunction with multiple sensors. However, application of autonomy in endoscopic surgery is very challenging, particularly in soft tissue work, due to the lack of high quality images and the unpredictable, constantly deforming environment. In this work, we propose a novel surgical perception framework, SuPer, for surgical robotic control. This framework continuously collects 3D geometric information that allows for mapping of a deformable surgical field while tracking rigid instruments within the field. To achieve this, a model-based tracker is employed to localize the surgical tool with a kinematic prior in conjunction with a model-free tracker to reconstruct the deformable environment and provide an estimated point cloud as a mapping of the environment. The proposed framework was implemented on the da Vinci Surgical R System in real-time with an endeffector controller where the target configurations are set and regulated through the framework. Our proposed framework successfully completed autonomous soft tissue manipulation tasks with high accuracy. The demonstration of this novel framework is promising for the future of surgical autonomy. In addition, we provide our dataset for further surgical research † .

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“…In later work, SIFT features proved to be insufficient for tracking due to the dynamic character (occlusions, blur, glare, deformation) of the environment [27]. To overcome that challenge, sensor-fusion using arthroscopic images, external camera and robot's odometry was employed to provide robust localisation for knee arthroscopy [27], [28]. Essentially, the non-visual sensory information enabled a dense feature mapping thanks to the underpinned localisation improvements.…”

Section: Surgical Visionmentioning

confidence: 99%

“…The first arthroscopic dataset (dataset 1) was acquired with a Stryker arthroscope and represents a challenging and realistic scenario complete with occlusions (tissue, water bubbles or surgical tools) and feature-poor images (see Remark 1). The second arthroscopic datasets (dataset 2), is the sequence 'H' from [28]. It was acquired using a PointGrey Camera and represents a simple case of an arthroscopic sequence where the camera images are not subject to realistic challenges such as occlusions.…”

Section: B Materials 1) Datasetmentioning

confidence: 99%

Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms

et al. 2020

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Navigation in endoscopic environments requires an accurate and robust localisation system. A key challenge in such environments is the paucity of visual features that hinders accurate tracking. This paper examines the performance of three image enhancement techniques for tracking under such feature-poor conditions including Contrast Limited Adaptive Histogram Specification (CLAHS), Fast Local Laplacian Filtering (LLAP) and a new combination of the two coined Local Laplacian of Specified Histograms (LLSH). Two cadaveric knee arthroscopic datasets and an underwater seabed inspection dataset are used for the analysis, where results are interpreted by defining visual saliency as the number of correctly matched key-point (SIFT and SURF) features. Experimental results show a significant improvement in contrast quality and feature matching performance when image enhancement techniques are used. Results also demonstrate the LLSHs ability to vastly improve SURF tracking performance indicating more than 87% of successfully matched frames. A comparative analysis provides some important insights useful in the design of vision-based navigation for autonomous agents in feature-poor environments.

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Dense-ArthroSLAM: Dense Intra-Articular 3-D Reconstruction With Robust Localization Prior for Arthroscopy

Cited by 33 publications

References 16 publications

DefSLAM: Tracking and Mapping of Deforming Scenes From Monocular Sequences

DefSLAM: Tracking and Mapping of Deforming Scenes From Monocular Sequences

SuPer: A Surgical Perception Framework for Endoscopic Tissue Manipulation With Surgical Robotics

Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms

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