Abstract — Recent work in the object recognition community has yielded a class of interest point-based features that are stable under significant changes in scale, viewpoint, and illumination, making them ideally suited to landmark-based navigation. Although many such features may be visible in a given view of the robot’s environment, only a few such features are necessary to estimate the robot’s position and orientation. In this paper, we address the problem of automatically selecting, from the entire set of features visible in the robot’s environment, the minimum (optimal) set by which the robot can navigate its environment. Specifically, we decompose the world into a small number of maximally sized regions such that at each position in a given region, the same small set of features is visible. We introduce a novel graph theoretic formulation of the problem and prove that it is NP-complete. Next, we introduce a number of approximation algorithms and evaluate them on both synthetic and real data. I.

In this paper we consider the problem of controlling the six degrees of freedom of a manipulator using the projection of 3D lines in the image plane of central catadioptric systems. Most of the e#ort in visual servoing are devoted to points, only few works have investigated the use of lines in visual servoing with traditional cameras and none has explored the case of omnidirectional cameras. First a generic interaction matrix for the projection of 3D straight lines is derived from the projection model of the entire class of central catadioptric cameras. Then an image-based control law is designed and validated through simulation results.

In this paper we consider the problem of controlling a robotic system using the projection of 3D lines in the image plane of central catadioptric systems. Most of the effort in visual servoing are devoted to points, only few works have investigated the use of lines in visual servoing with traditional cameras and none has explored the case of omnidirectional cameras. First a generic central catadioptric interaction matrix for the projection of 3D straight lines is derived from the projection model of an entire class of camera. Then an image-based control law is designed and validated through simulation results.

Abstract—Many vision-based navigation systems are restricted to the use of only a limited number of landmarks when computing the camera pose. This limitation is due to the overhead of detecting and tracking these landmarks along the image sequence. A new algorithm is proposed for subset selection from the available landmarks. This algorithm searches for the subset that yields minimal uncertainty for the obtained pose parameters. Navigation tasks have different types of goals: moving along a path, photographing an object for a long period of time, etc. The significance of the various pose parameters differs for different navigation tasks. Therefore, a requirements matrix is constructed from a supplied severity function, which defines the relative importance of each parameter. This knowledge can then be used to search for the subset that minimizes the uncertainty of the important parameters, possibly at the cost of greater uncertainty in others. It is shown that the task-oriented landmark selection problem can be defined as an integer-programming problem for which a very good approximation can be obtained. The problem is then translated into a semi-definite programming representation which can be rapidly solved. The feasibility and performance of the proposed algorithm is studied through simulations and lab experimentation. Index Terms—Covariance matrix, feature selection, landmarks, pose estimation, semi-definite programming (SDP). I.

Abstract — We present our approach for pose verification with monocular cameras in 3-dimensional space based on the image-based control paradigm. We describe the extensions to our previous control system for mobile navigation that allow us to estimate the complete set of pose parameters in space. The major contribution of this approach is a sensorindependent formulation of the image formation that allows a flexible configuration with a variety of sensor systems including standard cameras, omnidirectional cameras and laser systems. Our second contribution is a way to reinitialize the tracked landmarks during a multi-segment navigation in applications with significant deviations from the pre-taught trajectory as it is the case for handheld systems and flying robots. The presented system can be used as a guidance system for visitors. The localization is based on known landmarks that are in our case natural landmarks in the environment. These landmarks correspond to the satellites of a GPS system. We call it V-GPS (vision-based GPS) because of this similarity in the concept. A camera carried by a person allows to navigate along pre-specified paths through environments, like galleries, hospitals, parks, and other public places. I.

Abstract—In this paper, we consider the problem of controlling a6DOFholonomic robot and a nonholonomic mobile robot from the projection of 3-D straight lines in the image plane of central catadioptric systems. A generic central catadioptric interaction matrix for the projection of 3-D straight lines is derived using an unifying imaging model valid for an entire class of cameras. This result is exploited to design an image-based control law that allows us to control the 6 DOF of a robotic arm. Then, the projected lines are exploited to control a nonholonomic robot. We show that as when considering a robotic arm, the control objectives are mainly based on catadioptric image feature and that local asymptotic convergence is guaranteed. Simulation results and real experiments with a 6 DOF eye-to-hand system and a mobile robot illustrate the control strategy. Index Terms—3-D straight lines, omnidirectional vision, visual servoing. I.

In this paper we propose two algorithms for selecting landmarks, and methods to measure the reliability of the selected landmarks, that will be used in future work to optimize topological navigation of a blimp. The first algorithm evaluates image similarities between possible landmarks. The second one, which can also be used for online landmark selection, uses Incremental Principal Component Analysis and a measure of how good a new landmark can be expressed in the existing eigenspace.

Abstract. In this paper we consider the problem of controlling the six degrees of freedom of a manipulator using the projection of 3D lines in the image plane of central catadioptric systems. Most of the effort in visual servoing are devoted to points, only few works have investigated the use of lines in visual servoing with traditional cameras and none has explored the case of omnidirectional cameras. First a generic interaction matrix for the projection of 3D straight lines is derived from the projection model of the entire class of central catadioptric cameras. Then an image-based control law is designed and validated through simulation results. 1

The future aim of the work described in this report is appearance–based topological navigation of an autonomous aerial vehicle, namely a blimp, over an urban area. Here we only address the selection of good landmarks, which is one subproblem of the problems that still have to be solved in order to achieve topological navigation. Landmark selection methods try to optimize navigation by selecting distinctive features therefore minimizing localization errors. Common landmark selection algorithms select good landmarks in two steps. In the first step they detect possible landmarks, in the second step, they discard landmarks that are not reliable. The algorithms proposed in this report only deal with discarding unreliable landmarks. The first algorithm we called “profile–based algorithm ” evaluates pairwise image dissimilarity between possible landmarks and selects those landmarks that are as distinctive as possible. The second one called “IPCA–based algorithm ” incrementally updates an existing eigenspace by adding that landmark that can be expressed worst. Therefore an algorithm for Incremental Principal Component Analysis (IPCA) is used. Although not addressed here, this algorithm is capable to perform on–line landmark selection and can be used