Laser-Radar Data Fusion with Gaussian Process Implicit Surfaces

Gerardo-Castro, Marcos P.; Peynot, Thierry; Ramos, Fábio

doi:10.1007/978-3-319-07488-7_20

Cited by 20 publications

(15 citation statements)

References 16 publications

(25 reference statements)

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“…The mean of the GP is given by a signed geodesic distance, and its covariance is defined with a squared exponential driven by the Euclidean distance between voxels. Gaussian process implicit surfaces have been introduced previously by Williams et al [5] as a generalization of thin plate splines and used recently [6] for surface reconstruction. However, our approach combining geodesic and Euclidean distance functions for the mean and covariance is original and specifically suited to represent probabilistic image segmentations.…”

Section: Definitionmentioning

confidence: 99%

GPSSI: Gaussian Process for Sampling Segmentations of Images

Unkelbach

Ayache

et al. 2015

Lecture Notes in Computer Science

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Medical image segmentation is often a prerequisite for clinical applications. As an ill-posed problem, it leads to uncertain estimations of the region of interest which may have a significant impact on downstream applications, such as therapy planning. To quantify the uncertainty related to image segmentations, a classical approach is to measure the effect of using various plausible segmentations. In this paper, a method for producing such image segmentation samples from a single expert segmentation is introduced. A probability distribution of image segmentation boundaries is defined as a Gaussian process, which leads to segmentations that are spatially coherent and consistent with the presence of salient borders in the image. The proposed approach outperforms previous generative segmentation approaches, and segmentation samples can be generated efficiently. The sample variability is governed by a parameter which is correlated with a simple DICE score. We show how this approach can have multiple useful applications in the field of uncertainty quantification, and an illustration is provided in radiotherapy planning.

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Section: Definitionmentioning

confidence: 99%

GPSSI: Gaussian Process for Sampling Segmentations of Images

Unkelbach

Ayache

et al. 2015

Lecture Notes in Computer Science

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“…Due to only eliminating the obvious noise point of a large area manually during the data processing without smoothing and streamlining, the data processing errors are negligible. After the multi-sensor data fusion, the transformation error is decreased and can be also ignored relative to the measurement error of the laser tracker and the handheld laser scanning sensor [ 22 , 23 ]. The maximum length of the workpiece is 560 mm, and thus the measurement error is:

…”

Section: Error Analysis and Synthesismentioning

confidence: 99%

Fast Measurement and Reconstruction of Large Workpieces with Freeform Surfaces by Combining Local Scanning and Global Position Data

Chen

Zhang

et al. 2015

Sensors

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In this paper, we propose a new approach for the measurement and reconstruction of large workpieces with freeform surfaces. The system consists of a handheld laser scanning sensor and a position sensor. The laser scanning sensor is used to acquire the surface and geometry information, and the position sensor is utilized to unify the scanning sensors into a global coordinate system. The measurement process includes data collection, multi-sensor data fusion and surface reconstruction. With the multi-sensor data fusion, errors accumulated during the image alignment and registration process are minimized, and the measuring precision is significantly improved. After the dense accurate acquisition of the three-dimensional (3-D) coordinates, the surface is reconstructed using a commercial software piece, based on the Non-Uniform Rational B-Splines (NURBS) surface. The system has been evaluated, both qualitatively and quantitatively, using reference measurements provided by a commercial laser scanning sensor. The method has been applied for the reconstruction of a large gear rim and the accuracy is up to 0.0963 mm. The results prove that this new combined method is promising for measuring and reconstructing the large-scale objects with complex surface geometry. Compared with reported methods of large-scale shape measurement, it owns high freedom in motion, high precision and high measurement speed in a wide measurement range.

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“…GPIS has been used for terrain data [92] and 3D modeling [27], but is presently far too computationally expensive to run in real time.…”

Section: Dead Reckoning and Partial Constraintsmentioning

confidence: 99%

Toward autonomous underwater mapping in partially structured 3D environments

VanMiddlesworth¹

2014

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Motivated by inspection of complex underwater environments, we have developed a system for multi-sensor SLAM utilizing both structured and unstructured environmental features. We present a system for deriving planar constraints from sonar data, and jointly optimizing the vehicle and plane positions as nodes in a factor graph. We also present a system for outlier rejection and smoothing of 3D sonar data, and for generating loop closure constraints based on the alignment of smoothed submaps. Our factor graph SLAM backend combines loop closure constraints from sonar data with detections of visual fiducial markers from camera imagery, and produces an online estimate of the full vehicle trajectory and landmark positions. We evaluate our technique on an inspection of a decomissioned aircraft carrier, as well as synthetic data and controlled indoor experiments, demonstrating improved trajectory estimates and reduced reprojection error in the final 3D map. My research supervisor, John Leonard, has provided invaluable advice, mentoring, and access to an overwhelming array of research projects and other opportunities. He would be the village wise man or chief, in constant contact with everyone, always available for advice both personal and professional, identifying problems to be overcome in our hunting and gathering activities, and coordinating trade with nearby villages to ensure we had adequate supplies for the winter.To my advisor Dana Yoerger, whose contagious energy sparked a passion for marine robotics, whose battle-tested wisdom guided me at land and at sea, I owe my entire career at MIT. I see him as the village shaman, dispensing sage advice while magically solving any problem with his beyond-mortal mastery of arcane technical systems.Michael Kaess has been absolutely essential to my academic progress, and I imagine him as the village's technical wizard. He would be master of emerging technologies like metal tools and mechanical advantage, and would share his knowledge generously to ensure the success of the entire community.Franz Hover, the chief of the village next door, has provided both long-term opportunities and day-to-day mentoring and advice. I am immensely grateful for his help, and have very much enjoyed our collaboration on both research and operations.My family is always close to my heart, and their support would be crucial to surviving the harsh alpine environment (although the closeness might be su↵ocating if we were holed up in a tiny yurt made of animal skins all winter, with only a sooty fire for warmth and the village passtime of pebble-tossing for entertainment). My mother Diane would certainly stay up all night helping me study the various edible roots and practice animal-skinning technique for my rite of passage into manhood, a 5 twelve-day journey into the icy mountains with nothing but a flint knife and alpacaskin blanket. Over meals of small game, my father Rex and I would discuss the finer points of glacier navigation, and occasionally get into heated debates about village politi...

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Laser-Radar Data Fusion with Gaussian Process Implicit Surfaces

Cited by 20 publications

References 16 publications

GPSSI: Gaussian Process for Sampling Segmentations of Images

GPSSI: Gaussian Process for Sampling Segmentations of Images

Fast Measurement and Reconstruction of Large Workpieces with Freeform Surfaces by Combining Local Scanning and Global Position Data

Toward autonomous underwater mapping in partially structured 3D environments

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