1,807 research outputs found
Scan registration for autonomous mining vehicles using 3D-NDT
Scan registration is an essential subtask when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the shape of overlapping portions of the scans. This paper presents a new algorithm for registration of 3D data. The algorithm is a generalization and improvement of the normal distributions transform (NDT) for 2D data developed by Biber and Strasser, which allows for accurate registration using a memory-efficient representation of the scan surface. A detailed quantitative and qualitative comparison of the new algorithm with the 3D version of the popular ICP (iterative closest point) algorithm is presented. Results with actual mine data, some of which were collected with a new prototype 3D laser scanner, show that the presented algorithm is faster and slightly more reliable than the standard ICP algorithm for 3D registration, while using a more memory efficient scan surface representation
A Bayesian Framework for Human Body Pose Tracking from Depth Image Sequences
This paper addresses the problem of accurate and robust tracking of 3D human body pose from depth image sequences. Recovering the large number of degrees of freedom in human body movements from a depth image sequence is challenging due to the need to resolve the depth ambiguity caused by self-occlusions and the difficulty to recover from tracking failure. Human body poses could be estimated through model fitting using dense correspondences between depth data and an articulated human model (local optimization method). Although it usually achieves a high accuracy due to dense correspondences, it may fail to recover from tracking failure. Alternately, human pose may be reconstructed by detecting and tracking human body anatomical landmarks (key-points) based on low-level depth image analysis. While this method (key-point based method) is robust and recovers from tracking failure, its pose estimation accuracy depends solely on image-based localization accuracy of key-points. To address these limitations, we present a flexible Bayesian framework for integrating pose estimation results obtained by methods based on key-points and local optimization. Experimental results are shown and performance comparison is presented to demonstrate the effectiveness of the proposed approach
Development of a real-time full-field range imaging system
This article describes the development of a full-field range imaging system employing a high frequency amplitude modulated light source and image sensor. Depth images are produced at video frame rates in which each pixel in the image represents distance from the sensor to objects in the scene.
The various hardware subsystems are described as are the details about the firmware and software implementation for processing the images in real-time. The system is flexible in that precision can be traded off for decreased acquisition time. Results are reported to illustrate this versatility for both high-speed (reduced precision) and high-precision operating modes
Easy Rigging of Face by Automatic Registration and Transfer of Skinning Parameters
International audiencePreparing a facial mesh to be animated requires a laborious manualrigging process. The rig specifies how the input animation datadeforms the surface and allows artists to manipulate a character.We present a method that automatically rigs a facial mesh based onRadial Basis Functions and linear blend skinning approach.Our approach transfers the skinning parameters (feature points andtheir envelopes, ie. point-vertex weights),of a reference facial mesh (source) - already rigged - tothe chosen facial mesh (target) by computing an automaticregistration between the two meshes.There is no need to manually mark the correspondence between thesource and target mesh.As a result, inexperienced artists can automatically rig facial meshes and startright away animating their 3D characters, driven for instanceby motion capture data
Anatomically Constrained Video-CT Registration via the V-IMLOP Algorithm
Functional endoscopic sinus surgery (FESS) is a surgical procedure used to
treat acute cases of sinusitis and other sinus diseases. FESS is fast becoming
the preferred choice of treatment due to its minimally invasive nature.
However, due to the limited field of view of the endoscope, surgeons rely on
navigation systems to guide them within the nasal cavity. State of the art
navigation systems report registration accuracy of over 1mm, which is large
compared to the size of the nasal airways. We present an anatomically
constrained video-CT registration algorithm that incorporates multiple video
features. Our algorithm is robust in the presence of outliers. We also test our
algorithm on simulated and in-vivo data, and test its accuracy against
degrading initializations.Comment: 8 pages, 4 figures, MICCA
A robust extended H-infinity filtering approach to multi-robot cooperative localization in dynamic indoor environments
Multi-robot cooperative localization serves as an essential task for a team of mobile robots to work within an unknown environment. Based on the real-time laser scanning data interaction, a robust approach is proposed to obtain optimal multi-robot relative observations using the Metric-based Iterative Closest Point (MbICP) algorithm, which makes it possible to utilize the surrounding environment information directly instead of placing a localization-mark on the robots. To meet the demand of dealing with the inherent non-linearities existing in the multi-robot kinematic models and the relative observations, a robust extended Hā filtering (REHF) approach is developed for the multi-robot cooperative localization system, which could handle non-Gaussian process and measurement noises with respect to robot navigation in unknown dynamic scenes. Compared with the conventional multi-robot localization system using extended Kalman filtering (EKF) approach, the proposed filtering algorithm is capable of providing superior performance in a dynamic indoor environment with outlier disturbances. Both numerical experiments and experiments conducted for the Pioneer3-DX robots show that the proposed localization scheme is effective in improving both the accuracy and reliability of the performance within a complex environment.This work was supported inpart by the National Natural Science Foundation of China under grants 61075094, 61035005 and 61134009
3DFeat-Net: Weakly Supervised Local 3D Features for Point Cloud Registration
In this paper, we propose the 3DFeat-Net which learns both 3D feature
detector and descriptor for point cloud matching using weak supervision. Unlike
many existing works, we do not require manual annotation of matching point
clusters. Instead, we leverage on alignment and attention mechanisms to learn
feature correspondences from GPS/INS tagged 3D point clouds without explicitly
specifying them. We create training and benchmark outdoor Lidar datasets, and
experiments show that 3DFeat-Net obtains state-of-the-art performance on these
gravity-aligned datasets.Comment: 17 pages, 6 figures. Accepted in ECCV 201
Probabilistic Search for Object Segmentation and Recognition
The problem of searching for a model-based scene interpretation is analyzed
within a probabilistic framework. Object models are formulated as generative
models for range data of the scene. A new statistical criterion, the truncated
object probability, is introduced to infer an optimal sequence of object
hypotheses to be evaluated for their match to the data. The truncated
probability is partly determined by prior knowledge of the objects and partly
learned from data. Some experiments on sequence quality and object segmentation
and recognition from stereo data are presented. The article recovers classic
concepts from object recognition (grouping, geometric hashing, alignment) from
the probabilistic perspective and adds insight into the optimal ordering of
object hypotheses for evaluation. Moreover, it introduces point-relation
densities, a key component of the truncated probability, as statistical models
of local surface shape.Comment: 18 pages, 5 figure
Tensor Algebra: A Combinatorial Approach to the Projective Geometry of Figures
This paper explores the combinatorial aspects of symmetric and antisymmetric forms represented in tensor algebra. The development of geometric perspective gained from tensor algebra has resulted in the discovery of a novel projection operator for the Chow form of a curve in P3 with applications to computer vision
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