Simultaneous localization and mapping (SLAM): part II

Bailey, Tim; Durrant‐Whyte, Hugh

doi:10.1109/mra.2006.1678144

Cited by 2,100 publications

(993 citation statements)

References 45 publications

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“…Extensive research has been undertaken over the past two decades, creating a large variety of solutions. A comprehensive review can be found in (Durrant-Whyte and Bailey and Durrant-Whyte, 2006). The most common probabilistic frameworks are the Extended Kalman Filter (EKF), the Particle Filter and the Rao-Blackwellised Filter.…”

Section: State-of-the-artmentioning

confidence: 99%

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

Maier-Hein

Mountney

Bartoli

et al. 2013

Medical Image Analysis

242

130

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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 capabilites 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 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 optical imaging, this is a timely discussion about technologies that could facilitate complex CAS procedures in dynamic and deformable anatomical regions.

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Section: State-of-the-artmentioning

confidence: 99%

Optical techniques for 3D surface reconstruction in computer-assisted laparoscopic surgery

Maier-Hein

Mountney

Bartoli

et al. 2013

Medical Image Analysis

242

130

View full text Add to dashboard Cite

show abstract

“…Among others, 3-D modeling is a key component for the acquisition of virtual 3-D models from real objects, the digitalization of archaeological buildings or sculptures for restoration planning or archival storage [11], and the construction of environment maps in robot or vehicle navigation [19,28]. In particular, in the field of robotics, there is an increasing interest in both 3-D environment reconstruction and simultaneous localization and mapping (SLAM) solutions [2,6,32].…”

Section: Introductionmentioning

confidence: 99%

Real-Time RGB-D Mapping and 3-D Modeling on the GPU Using the Random Ball Cover

Bauer

Wasza

Lugauer

et al. 2013

Consumer Depth Cameras for Computer Vision

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In this chapter, we present a system for real-time point cloud mapping and scene reconstruction based on an efficient implementation of the iterative closest point (ICP) algorithm on the graphics processing unit (GPU). Compared to state-ofthe-art approaches that achieve real-time performance using projective data association schemes which operate on the 3-D scene geometry solely, our method allows to incorporate additional complementary information to guide the registration process. In this work, the ICP's nearest neighbor search evaluates both geometric and photometric information in a direct manner, achieving robust mappings in real-time. In order to overcome the performance bottleneck in nearest neighbor search space traversal, we exploit the inherent computation parallelism of GPUs. In particular, we have adapted the random ball cover (RBC) data structure and search algorithm, originally proposed for high-dimensional problems, to low-dimensional RGB-D data. The system is validated on scene and object reconstruction scenarios. Our implementation achieves frame-to-frame registration runtimes of less than 20 ms on an off-the-shelf consumer GPU.

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“…Simultaneous localization and mapping (SLAM) is the process of building a map of an environment while concurrently generating an estimate for the location of the robot (also called "robot pose"). SLAM has been an active research topic for the past decade due to its numerous applications [1].…”

Section: Introductionmentioning

confidence: 99%

“…In local submap joining, a sequence of small sized local submaps are built by a SLAM algorithm (e.g. EKF SLAM [7] or maximal likelihood (ML) approach 1 ) This work is supported in part by the ARC Centre of Excellence programme, funded by the Australian Research Council (ARC) and the New South Wales State Government.…”

Section: Introductionmentioning

confidence: 99%