The focus of this paper lies on the creation of 3D hu- man skeleton from a set of 2D images. Unlike available approaches, which utilize a single 2D image for 3D recon- struction, the prosecuted approach utilities a set of multi- ple images, which are obtained from a simple query to the google image database. We only assume, that a query key- word can be linked to a set of images, which contain a rep- resentative subset related to the query. We expect the data to also contain false (i.e. non human-pose related) images. Our approach uses a human-pose based 3D shape context model for matching human-poses in 3D space, and filter them using a hierarchical binary clustering approach. The performance of this approach is evaluated using different query keywords.

This paper describes a fast plane extraction algorithm for 3D range data. Taking advantage of the point neighborhood structure in data acquired from 3D sensors like range cameras, laser range finders and Microsoft Kinect, it divides the plane-segment extraction task into three steps. The first step is a 2D line segment extraction from raw sensor data, interpreted as 2D data, followed by a line segment based connected component search. The final step finds planes based on connected segment component sets. The first step inspects 2D sub spaces only, leading to a line segment representation of the 3D scan. Connected components of segments represent candidate sets of coplanar segments. Line segment representation and connected components vastly reduce the search space for the plane-extraction step. A region growing algorithm is utilized to find coplanar segments and their optimal (least square error) plane approximation. Region growing contains a fast plane update technique in its core, which combines sets of co-planar segments to form planar elements. Experiments are performed on real world data from different sensors.

We present a system for 2D robot mapping which is entirely based on line segment representation of the environment. The system consists of multiple modules, i.e. scan number reduction, global scan alignment, scan merging and segment-error filtering, which give an example of the simplicity of mid level data processing and the advanced possibilities opened by segment based design. The compact segment representation enables creation and optimization of a global pose graph for scan registration, which is the core of the mapping system. Experiments verify the applicability to real world data sets and lead to very compact maps, which represent single linear features, e.g. walls, with single line segments.

Line segment based representation of 2D robot maps is known to have advantages over raw point data or grid based representation gained from laser range scans. It significantly reduces the size of the data set. It also contains higher geometric information, which is necessary for robust post processing. The paper describes an algorithm to convert global 2D robot maps to line segment representation, using a pre-aligned set of point-based single scans as input. Mean-shift clustering on the set of all line segments is utilized to merge perceptually similar segments to single instances: locally linear features in the environment are unambiguously represented by single line segments in the final global map. Apart from a scaling parameter, the approach is parameter free. Experiments on real world data sets prove its applicability in the field of robot mapping.

This paper addresses the problem of piecewise linear approximation of point sets without any constraints on the order of data points or the number of model components (line segments). We point out two problems with the maximum likelihood estimate (MLE) that present serious drawbacks in practical applications. One is that the parametric models obtained using a classical MLE framework are not guaranteed to be close to data points. It is typically impossible, in this classical framework, to detect whether a parametric model fits the data well or not. The second problem is related to accurately choosing the optimal number of model components. We first fit a nonparametric density to the data points and use it to define a neighborhood of the data. Observations inside this neighborhood are deemed informative; those outside the neighborhood are deemed uninformative for our purpose. This provides us with a means to recognize when models fail to properly fit the data. We then obtain maximum likelihood estimates by optimizing the Kullback-Leibler Divergence (KLD) between the nonparametric data density restricted to this neighborhood and a mixture of parametric models. We prove that, under the assumption of a reasonably large sample size, the inferred model components are close to their ground-truth model component counterparts. This holds independently of the initial number of assumed model components or their associated parameters. Moreover, in the proposed approach, we are able to estimate the number of significant model components without any additional computation.

Given two shapes, the correspondence between distinct visual features is the basis for most alignment processes and shape similarity measures. This paper presents an approach introducing particle filters to establish perceptually correct correspondences between point sets representing shapes. Local shape feature descriptors are used to establish correspondence probabilities. The global correspondence structure is calculated using additional constraints based on domain knowledge. Domain knowledge is characterized as prior distributions expressing hypotheses about the global relationships between shapes. These hypotheses are generated during the iterative particle filtering process. Experiments using standard alignment techniques, based on the given correspondence relationships, demonstrate the advantages of this approach.

Merging local maps, acquired by multiple robots, into a global map, (also known as map merging) is one of the important issues faced by virtually all cooperative exploration techniques. We present a novel and simple solution to the problem of map merging by reducing it to the problem of SLAM of a single ¿virtual¿ robot. The individual local maps and their shape information constitute the sensor information for the virtual robot. This approach allows us to adapt the framework of Rao-Blackwellized particle filtering used in SLAM of a single robot for the problem of map merging.

The paper shows how particle filters can be used to establish visually consistent partial correspondences between similar features in unrestricted 2D point sets representing shapes. Given an update rule, the PF system has the advantage that global constraints can be learned. We motivate and define the update rule for the given task and show its superior performance in comparison to a globally unrestricted approach.

We present a system to increase the performance of feature correspondence based alignment algorithms for laser scan data. Alignment approaches for robot mapping, like ICP or FFS, perform successfully only under the condition of sufficient overlap of features between individual scans. This condition is often not met, for example in sparsely scanned environments or disaster areas for search and rescue robot tasks. Assuming mid level world knowledge (in the presented case, weak presence of noisy, roughly linear or rectangular-like objects) our system augments the sensor data with hypotheses (dasiaVirtual Scanspsila) about ideal models of these objects. These hypotheses are generated by analyzing the current aligned map estimated by the underlying iterative alignment algorithm. The augmented data is used to improve the alignment process. Feedback between the data alignment and the data analysis confirms, modifies, or discards the Virtual Scans in each iteration. Experiments with a simulated scenario and real world data from a rescue robot scenario show the applicability and advantages of the approach.

The paper deals with grouping of edges to contours of shapes using only local symmetry and continuity. Shape skeletons are used to generate the search space for a version of the Markov Chain Monte Carlo approach utilizing particle filters to find the most likely skeleton. Intuitively this means that grouping of edge segments is performed by walking along the skeleton. The particle search, which is an adapted version of a successful algorithm in robot mapping, is assisted by a reference model of a shape, which is expressed as the sequence of sample points and radii of maximal skeleton disks. This model is sufficiently flexible to represent non-rigid deformations, but restrictive enough to perform well on real, noisy image data. The order of skeleton points (and their corresponding segments) found by the particles defines the grouping.

Abstract – The paper deals with fitting of planar patches to 3D laser range data obtained by a mobile robot. The number and the initial position of the patches are unknown, hence their estimation is a challenging problem. It is solved by adding iterated steps of split and merge to a modified Expectation Maximization (EM) algorithm. This allows for precise adjustment of the number of patches, independent from the initial model. The proposed approach overcomes the problem of classical EM, which produces an optimal solution only if the number and position of model components is well estimated. Index Terms- 3D Robot Mapping, EM I.

Our goal is polygonal approximation of laser range data points obtained by a mobile robot. The proposed approach provides a precise estimation of the number of model components (line segments) and their initial parameters independent of their initial values. We use principles of perceptual grouping to evaluate the approximation quality obtained in each expectation maximization (EM) step. By evaluating EM approximation quality we are able to recognize a locally optimal solution, and modify the number of model components and their parameters. Consequently, EM can converge only to a globally optimal solution independent of the initial number of model components and their initial parameters

We introduce a new EM framework in which it is possible not only to optimize the model parameters but also the number of model components. A key feature of our approach is that we use nonparametric density estimation to improve parametric density estimation in the EM framework. While the classical EM algorithm estimates model parameters empirically using the data points themselves, we estimate them using nonparametric density estimates.There exist many possible applications that require optimal adjustment of model components. We present experimental results in two domains. One is polygonal approximation of laser range data, which is an active research topic in robot navigation. The other is grouping of edge pixels to contour boundaries, which still belongs to unsolved problems in computer vision.

We consider the problem of elastic matching of time series. We propose an algorithm that determines a subsequence of a target time series that best matches a query series. In the proposed algorithm, we map the problem of the best matching subsequence to the problem of a cheapest path in a DAG (directed acyclic graph). The proposed approach allows us to also compute the optimal scale and translation of time series values, which is a nontrivial problem in the case of subsequence matching.

The purpose of this paper is to present a technique to create a global map of robots' surroundings by converting the raw data acquired from a scanning sensor to a compact map composed of just a few generalized polylines (polygonal curves). We propose a new approach to merging robots' maps that is composed of a local geometric process of merging similar line segments (termed discrete segment evolution) with a global statistical control process. In the case of single robot, we are able to incrementally build a map showing the environment the robot has traveled through by merging its polygonal map with actual scans. In the case of a robot team, we are able to identify common parts of their partial maps and if common parts are present construct a joint map of the explored environment.

The Core Experiment CE-Shape-1 for shape descriptors performed for the MPEG-7 standard gave a unique opportunity to compare various shape descriptors for non-rigid shapes with a single closed contour. There are two main differences with respect to other comparison results reported in the literature: (1) For each shape descriptor the experiments were carried out by an institute that is in favor of this descriptor. This implies that the parameters for each system were optimally determined and the implementations were thoroughly rested. (2) It was possible to compare the performance of shape descriptors based on totally different mathematical approaches. A more theoretical comparison of these descriptors seems to be extremely hard. In this paper we report on the MPEG-7 Core Experiment CE-Shape.

A cognitively motivated similarity measure is presented and its properties are analyzed with respect to retrieval of similar objects in image databases of silhouettes of 2D objects. To reduce influence of digitization noise, as well as segmentation errors, the shapes are simplified by a novel process of digital curve evolution. To compute our similarity measure, we first establish the best possible correspondence of visual parts (without explicitly computing the visual parts). Then, the similarity between corresponding parts is computed and aggregated. We applied our similarity measure to shape matching of object contours in various image databases and compared it to well-known approaches in the literature. The experimental results justify that our shape matching procedure gives an intuitive shape correspondence and is stable with respect to noise distortions.

A similarity measure for silhouettes of 2D objects is presented, and its properties are analyzed with respect to retrieval of similar objects in an image database. Our measure profits from a novel approach to subdivision of objects into parts of visual form. To compute our similarity measure, we first establish the best possible correspondence of visual parts, which is based on a correspondence of convex boundary arcs. Then the similarity between corresponding arcs is computed and aggregated. We applied our similarity measure to shape matching of object contours in various image databases and compared it to well-known approaches in the literature. The experimental results justify that our shape matching procedure gives an intuitive shape correspondence and is stable with respect to noise distortions.