1,739 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
Scan matching by cross-correlation and differential evolution
Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85
Visual-LiDAR Odometry and Mapping with Monocular Scale Correction and Motion Compensation
This paper presents a novel visual-LiDAR odometry and mapping method with
low-drift characteristics. The proposed method is based on two popular
approaches, ORB-SLAM and A-LOAM, with monocular scale correction and
visual-assisted LiDAR motion compensation modifications. The scale corrector
calculates the proportion between the depth of image keypoints recovered by
triangulation and that provided by LiDAR, using an outlier rejection process
for accuracy improvement. Concerning LiDAR motion compensation, the visual
odometry approach gives the initial guesses of LiDAR motions for better
performance. This methodology is not only applicable to high-resolution LiDAR
but can also adapt to low-resolution LiDAR. To evaluate the proposed SLAM
system's robustness and accuracy, we conducted experiments on the KITTI
Odometry and S3E datasets. Experimental results illustrate that our method
significantly outperforms standalone ORB-SLAM2 and A-LOAM. Furthermore,
regarding the accuracy of visual odometry with scale correction, our method
performs similarly to the stereo-mode ORB-SLAM2.Comment: 7 pages, 7 figures, 31 reference
Aerial-Ground collaborative sensing: Third-Person view for teleoperation
Rapid deployment and operation are key requirements in time critical
application, such as Search and Rescue (SaR). Efficiently teleoperated ground
robots can support first-responders in such situations. However, first-person
view teleoperation is sub-optimal in difficult terrains, while a third-person
perspective can drastically increase teleoperation performance. Here, we
propose a Micro Aerial Vehicle (MAV)-based system that can autonomously provide
third-person perspective to ground robots. While our approach is based on local
visual servoing, it further leverages the global localization of several ground
robots to seamlessly transfer between these ground robots in GPS-denied
environments. Therewith one MAV can support multiple ground robots on a demand
basis. Furthermore, our system enables different visual detection regimes, and
enhanced operability, and return-home functionality. We evaluate our system in
real-world SaR scenarios.Comment: Accepted for publication in 2018 IEEE International Symposium on
Safety, Security and Rescue Robotics (SSRR
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