No abstract available.

Once a multi-robot system is implemented on real hardware and tested in the real world, analyzing its evolution and debugging unexpected behaviors is often a very difficult task. We present a tool for aiding this activity, by visualizing an Augmented Reality overlay on a live video feed acquired by a fixed camera overlooking the robot environment. Such overlay displays live information exposed by each robot, which may be textual (state messages), symbolic (graphs, charts), or, most importantly, spatially-situated; spatially-situated information is related to the environment surrounding the robot itself, such as for example the perceived position of neighboring robots, the perceived extent of obstacles, the path the robot plans to follow. We show that, by directly representing such information on the environment it refers to, our proposal removes a layer of indirection and significantly eases the process of understanding complex multi-robot systems. We describe how the system is implemented, discuss application examples in different scenarios, and provide supplementary material including demonstration videos and a functional implementation.

Small-footprint mobile ground robots, such as the popular Turtlebot and Kobuki platforms, are by necessity equipped with sensors which lie close to the ground. Reliably detecting and tracking people from this viewpoint is a challenging problem, whose solution is a key requirement for many applications involving sharing of common spaces and close human-robot interaction. We present a robust solution for cluttered indoor environments, using an inexpensive RGB-D sensor such as the Microsoft Kinect or Asus Xtion. Even in challenging scenarios with multiple people in view at once and occluding each other, our system solves the person detection problem significantly better than alternative approaches, reaching a precision, recall and F1-score of 0.85, 0.81 and 0.83, respectively. Evaluation datasets, a real-time ROS-enabled implementation and demonstration videos are provided as supplementary material.

Photographic images of moving objects are often characterized by motion blur; analyzing motion blurred images is problematic since the moving ob-ject boundaries appear fuzzy and seamlessly blend with the background. In extreme cases, when the object motion is fast in relation to the exposure time, the blurred object image becomes an elongated, semitransparent smear. We consider a motion-blurred color image of an object moving over a still background: we introduce meaningful entities, the “alpha map ” and the “color map”, which bear information about the object motion during the ex-posure, and its color and texture; we draw connections to the well-known alpha matting problem, providing an original interpretation in this context; we present an analytic technique for extracting the two maps under assump-tions on the background and object colors, and explore the relaxation of these assumptions. We provide experimental results on both synthetic and real im-ages, which confirm the correctness of our approach, and describe diverse ap-plication examples in fields spanning from 3D reconstruction to image/video enhancement. 1

In this paper we address the problem of sphere localization from a single image taken with a noncentral catadioptric camera. We propose a method for determining both the radius and the position of an unknown sphere from a single, catadioptric image. The method can find its application in the field of robotic vision, especially in mobile robots playing soccer in RoboCup contests, in order to improve robot capabilities related to playing with a flying ball. Recently, a method for sphere reconstruction from single image taken with a noncentral, axial-symmetric, catadioptric camera has been proposed. In an axial symmetric catadioptric cameras, the pinhole of the camera is placed on the mirror axis. Though axial symmetric cameras help to simplify the geometrical treatment of the problem, they are difficult to set up since they require a precise alignment, usually hard to check. In this paper we deal with the general case of off-axis catadioptric cameras, with the camera pinhole placed in a general position w.r.t. the mirror. We devise a simple geometrical method by which we determine both the position of a sphere and its radius from its apparent image contour. Since our approach is based on coplanar viewing rays, it has a wider applicability w.r.t. the previous method, as it relaxes the constraint on camera position, i.e. it does not require a precise alignment, and the constraint on mirror axial symmetry, i.e. it can be applied to a wider class of mirrors. Some preliminary experiments both on simulated and real image are also presented.

Line localization from a single image of a central camera is an ill-posed problem unless other constraints or apriori knowledge are exploited. Recently, it has been proved that noncentral catadioptric cameras allow space lines to be localized from a single image. In this paper we propose two novel localization algorithms. The first method exploits a pair of coplanar viewing rays to localize the space line. The second method follows a constrained non-linear minimization procedure using a suitable parametrization to represent space lines. We compare the accuracy of the proposed method w.r.t. the classical line localization algorithm and two robust variants of it. We carried out both synthetic and real experiments and evaluated the performance in localizing a set of space lines. We also propose a quality index for the viewing surfaces associated to space lines in order to better evaluate the quality of the localization. The experimental results showed the effectiveness and the accuracy of both proposed methods.

We present a novel calibration method for off-axis catadioptric cameras, i.e. standard perspective cameras placed in a generic position w.r.t. an axial-symmetric mirror of unknown shape. The proposed method estimates the intrinsic parameters of the natural perspective camera, the 3D shape of the mirror and its pose w.r.t. the camera. The peculiarity of our approach is that, unlike several other calibration methods, we do not require any cross section of the mirror to be visible in the image. Instead, we require that the catadioptric image contains at least the image of one generic space line. We then derive some constraints that, combined with the harmonic homology relating the apparent contours of the mirror, allow us to calibrate the off-axis camera. We provide experimental results both on synthetic and camera images that prove the validity of the technique.

In-door environments often contain several straight line segments. The 3D reconstruction of such environments can thus reduce to the localization of lines in the 3D space. Multi-view reconstruction requires the solution of the correspondence problem. The use of a single image to localize space lines is attractive, since the correspondence problem can be avoided. However, using a perspective camera (or a central one), a line can not be localized, since its viewing surface is planar, and hence it can contain infinite lines other than the correct one. In this paper we study the number of planar viewing surfaces for a general class of catadioptric cameras, constituted by an axial symmetric mirror and a perspective camera placed at generic relative position. We show that, under broad conditions, there is only a discrete set of planar viewing surfaces for the considered class of cameras. This result establishes a qualitative difference with respect to axial-symmetric cameras (e.g., catadioptric cameras constituted by an axial-symmetric mirror plus a perspective camera, whose viewpoint is constrained to be on the mirror axis), where an infinite set of planar viewing surfaces exists. Then, some conditions are derived for the localization of lines in the 3D space from single images. Preliminary experiments are also reported.

The reconstruction of 3D scenes constituted by straight lines can find many applications both in computer vision and in mobile robotics. Most of the approaches to this problem involve either stereo-vision or the analysis of a sequence of images taken from different viewpoints: in both cases, the solution of the correspondence problem is required. This paper studies the localization of straight lines in 3D space from single ID images, acquired by a catadioptric camera. In general, using a noncentral camera, the viewing rays starting from the points of a straight line constitute a non-planar surface: if this non-planar surface only contains one straight line, other than the viewing rays, then the straight line can univocally be localized. Some conditions for the univocal localization of straight lines are derived. A simple technique for the straight line localization is presented, and some preliminary experimental results are discussed.

Indexing is a well-known paradigm for object recognition. In indexing, each 3D model is represented as the set of values assumed by a given vector of image parameters in correspondence to all the possible images of the 3D model. An open problem, posed by Jacobs, concerned the minimum dimensionality of such sets under perspective. This paper proves that, under calibrated or uncalibrated perspective, the minimum dimensionality of the set representing any 3D modeled point-set is two. Two-dimensional representations are found also for 3D curved objects.

A criterion is presented for the automatic selection of a sensor measurement aimed at observing the state of a system which is described both by discrete variables and by continuous ones. The criterion is based on the expected value of the entropy variation associated to the sensor observation. This criterion is then applied to object recognition and localization tasks, in which the observed system is characterized by the object class, represented by a discrete variable, and by the object pose, i.e., position and orientation, represented by a vector of continuous parameters. The proposed criterion also accounts for the information obtained in the case the observed object is missed by the measurement.

For pt. 1 see ibid. An entropic criterion for minimum uncertainty sensing, introduced in the companion paper, is applied to a case study related to the localization and recognition of a polygonal object by means of an orientable range finder. The observed object is characterized by two different uncertain parameters: the pose (position and orientation) of the object and its identity. A priori, only partial information is available both on the object identity and on its pose. Additional information about the observed object is acquired by the orientable range finder activated according to the above criterion.

The space requirements for indexing under perspective projections are addressed. It is known that the surface representing the set of possible images of a model point set within the index space must be three-dimensional (Jacobs, 1996). Under affine projections, the representing surface can be factorized as the cartesian product of lower-dimensional surfaces: these are obtained by projecting the representing surface onto orthogonal subspaces of the index space (Jacobs, 1992; Weinshall, 1993). This paper shows that, under perspective, such a factorization does not exist, yielding a negative answer to a question left open in (Jacobs, 1996). However, it is shown that there exist subspaces of the index space, onto which the representing surface projection is two-dimensional.

The self-localization of a mobile robot within a known environment, by means of an orientable range finder, is considered. The problem of the determination of the sensor orientation which minimizes the position uncertainty of the mobile robot is addressed. An efficient technique is proposed to determine the optimal sensor exploration, given the current robot position estimate and its uncertainty. Once a tentative exploration is given, the technique avoids to take any worst exploration into account, allowing to efficiently determine the optimal one. Both location accuracy and efficiency have been analyzed in the paper. The time needed to plan the exploration is found to be well below the time needed for the sensor activation. The technique is demonstrated by experimental results on environments containing curvilinear parts.

This work proposes a methodology for the analysis of the uncertainty in the localization of objects when considering uncertain image data, camera and object geometry parameters. The uncertainty is propagated through an extended static Kalman filter initialized with the parameters used for the localization and updated with new matched features obtained by back-projecting onto the image. At the end of the process, a better estimate of the object pose with its uncertainty is given along with a new estimate of the used uncertain object features and the camera parameters. The methodology is now in use in an object localization system. 1 Introduction The role of uncertainty is very important where a measure of the position and orientation of a modeled object in space is required. Besides providing information on the reliability of the data, the reason for using the uncertainty of the sensory data is for improving the estimate of the parameters we want to measure. This improvement is obtaine...

A straightforward method is presented for the determination of the optimal sensor exploration in the localization of a polyhedral object, whose geometry is known. Optimality is intended in the sense of the a posteriori covariance matrix of the object position and orientation parameters. The method consists in decomposing the problem into simpler subproblems, each one relative to a single planar face of the object. It requires reasonable processing time, i.e. comparable with the sensor activation time.>

A criterion is presented for the automatic selection of a sensor detection aimed at observing the state of a system, which is described both by discrete variables and by continuous ones. The criterion is based on the expected value of the entropy variation relative to the transition associated to the sensor observation. This criterion is then applied to object recognition and localization tasks, in which the observed system is described by object class (i.e., a discrete variable) and by the object position (i.e. a vector of continuous parameters). Finally, a simple example is discussed.