Large scale multiple robot visual mapping with heterogeneous landmarks in semi-structured terrain

We present a common framework for accurate and automatic registration of two geometrically complex 3D range scans by using linear or planar features. The linear features of a range scan are extracted with an efficient split-and-merge line-fitting algorithm, which refines 2D edges extracted from the associated reflectance image considering the corresponding 3D depth information. The planar features are extracted employing a robust planar segmentation method, which partitions a range image into a set of planar patches. We propose an efficient probability-based RANSAC algorithm to automatically register two overlapping range scans. Our algorithm searches for matching pairs of linear (planar) features in the two range scans leading to good alignments. Line orientation (plane normal) angles and line (plane) distances formed by pairs of linear (planar) features are invariant with respect to the rigid transformation and are utilized to find candidate matches. To efficiently seek for candidate pairs and groups of matched features we build a fast search codebook. Given two sets of matched features, the rigid transformation between two scans is computed by using iterative linear optimization algorithms. The efficiency and accuracy of our registration algorithm were evaluated on several challenging range data sets.

Automatic scan registration using 3D linear and planar features

Motion estimation is a very important problem in dynamic scene analysis. Although it is easier to estimate motion parameters from 3D data than from 2D images, it is not trivial, since the 3D data we have are almost always corrupted by noise. A comparative study on motion estimation from 3D line segments is presented. Two representations of line segments and two representations of rotation are described. With different representations of line segments and rotation a number of methods for motion estimation are presented, including the extended Kalman filter a general minimization process and the singular value decomposition. These methods are compared using both synthetic and real data obtained by a trinocular stereo. It is observed that the extended Kalman filter with the rotation axis representation of rotation is preferable. Note that all methods discussed can be directly applied to 3D point data.

Determining motion from 3D line segment matches: a comparative study.

Faulty postures, scoliosis and sagittal plane deformities should be detected as early as possible to apply preventive and treatment measures against major clinical consequences. To support documentation of the severity of deformity and diminish x-ray exposures, several solutions utilizing analysis of back surface topography data were introduced. A novel approach to automatic recognition and localization of anatomical landmarks of the human back is presented that may provide more repeatable results and speed up the whole procedure. The algorithm was designed as a two-step process involving a statistical model built upon expert knowledge and analysis of three-dimensional back surface shape data. Voronoi diagram is used to connect mean geometric relations, which provide a first approximation of the positions, with surface curvature distribution, which further guides the recognition process and gives final locations of landmarks. Positions obtained using the developed algorithms are validated with respect to accuracy of manual landmark indication by experts. Preliminary validation proved that the landmarks were localized correctly, with accuracy depending mostly on the characteristics of a given structure. It was concluded that recognition should mainly take into account the shape of the back surface, putting as little emphasis on the statistical approximation as possible.

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Automatic recognition of surface landmarks of anatomical structures of back and posture.

We present an automated method for the tracking and dynamics modeling of microtubules -a major component of the cytoskeleton- which provides researchers with a previously unattainable level of data analysis and quantification capabilities. The proposed method improves upon the manual tracking and analysis techniques by i) increasing accuracy and quantified sample size in data collection, ii) eliminating user bias and standardizing analysis, iii) making available new features that are impractical to capture manually, iv) enabling statistical extraction of dynamics patterns from cellular processes, and v) greatly reducing required time for entire studies. An automated procedure is proposed to track each resolvable microtubule, whose aggregate activity is then modeled by mixtures of Hidden Markov Models to uncover dynamics patterns of underlying cellular and experimental conditions. Our results support manually established findings on an actual microtubule dataset and illustrate how automated analysis of spatial and temporal patterns offers previously unattainable insights to cellular processes.

/pdf/activity-analysis-in-microtubule-videos-by-mixture-of-hidden-5df2t1k9bm.pdf

Activity Analysis in Microtubule Videos by Mixture of Hidden Markov Models

Matching two sets of lines is a basic tool that has applications in many computer vision problems such as scene registration, object recognition, motion estimation, and others. Line sets may be composed of infinitely long lines or finite length line segments. Depending on line lengths, three basic cases arise in matching sets of lines: 1) finite-finite, 2) finite-infinite, and 3) infinite-infinite. Case 2 has not been treated in the literature. For Cases 1 and 3, existing algorithms for matching 3D line sets are not completely satisfactory in that they either solve special situations, or give approximate solutions, or may not converge, or are not invariant with respect to coordinate system transforms. In this paper, we present new algorithms that solve exactly all three cases for the general situation. The algorithms are provably convergent and invariant to coordinate transforms. Experiments with synthetic and real 3D image data are reported.

B. Kamgar-Parsi

Papers

Algorithms for matching 3D line sets