Bart Lamiroy

Object Pose: The Link between Weak Perspective,Paraperspective, and Full Perspective

Recently, DeMenthon and Davis (1992, 1995) proposed a method for determining the pose of a 3-D object with respect to a camera from 3-D to 2-D point correspondences. The method consists of iteratively improving the pose computed with a weak perspective camera model to converge, at the limit, to a pose estimation computed with a perspective camera model. In this paper we give an algebraic derivation of DeMenthon and Davis method and we show that it belongs to a larger class of methods where the perspective camera model is approximated either at zero order (weak perspective) or first order (paraperspective). We describe in detail an iterative paraperspective pose computation method for both non coplanar and coplanar object points. We analyse the convergence of these methods and we conclude that the iterative paraperspective method (proposed in this paper) has better convergence properties than the iterative weak perspective method. We introduce a simple way of taking into account the orthogonality constraint associated with the rotation matrix. We analyse the sensitivity to camera calibration errors and we define the optimal experimental setup with respect to imprecise camera calibration. We compare the results obtained with this method and with a non-linear optimization method.

Rapid Object Indexing and Recognition Using Enhanced Geometric Hashing

We address the problem of 3D object recognition from a single 2D image using a model database. We develop a new method called enhanced geometric hashing. This approach allows us to solve for the indexing and the matching problem in one pass with linear complexity. Use of quasi-invariants allows us to index images in a new type of geometric hashing table. They include topological information of the observed objects inducing a high numerical stability.

Controlling robots with two cameras: how to do it properly

We address visual servoing through a fixed stereo rig using an image Jacobian. Existing methods are based on the stacking of monocular servo image Jacobians, resulting in largely over-constrained control commands. In this paper we formally show that the epipolar constraint between two images can be taken into account explicitly. We then show that use of stereo significantly increases the quality of the servo task execution, especially where precision, robustness and smoothness of movement is concerned.

Object pose: links between paraperspective and perspective

D.F. Dementhon and L.S. Davis (1995) proposed a method for determining the pose of a 3D object with respect to a camera from 3D to 2D point correspondences. The method consists of iteratively improving the pose computed with a weak perspective camera model to converge at the limit, to a pose estimation computed with a perspective camera model. We show that the method of Dementhon and Davis can be extended to paraperspective. The iterative paraperspective pose algorithm that we describe in detail has interesting properties both in terms of speed and rate of convergence. Moreover, we introduce a simple way of taking into account the orthogonality constraint associated with the rotation matrix and we define the optimal experimental setup to be used in the presence of camera calibration errors.<<ETX>>

What metric stereo can do for visual servoing

This paper describes a number of geometric tools that can be easily implemented in order to obtain a very robust visual servoing platform. Most often, visual servoing algorithms are either based on calibrated systems or dispose of enough information to self-calibrate. We show that this abundance of information can be exploited in the case of stereo servoing as to offer a solution for some problems related to signal loss such as temporary occlusions, specularities or CAD tracking difficulties. The presented servoing algorithm, can cope with large image perturbations and is able to control a robot through a non modeled set of reference points.

An Image Oriented CAD Approach

Matching abstract CAD models with images is a well studied problem. It includes the problems of identifying modelled objects for which 3D CAD data is available in images, and of locating them with respect to a given reference frame. Some authors have concluded that this problem has no real general solution as the representation levels are too different (see for instance the discussion in the workshop of CAD model-based vision [Bow91]).

Combining Local Recognition Methods for Better Recognition.

In this paper we propose a comprehensive framework that allows existing local appearance methods to collaborate in order to overcome their mutual drawbacks. Our approach tends to use the best suited local descriptors for a recognition task, and is capable of combining evidence of different methods in the case where no clearly superior type of descriptor exists. We achieve this collaboration by locally matching geometric configurations and let each match contribute to the computation of the apparent motion between a model image and the unknown query image. We show in this paper that, if we have a set of local methods conforming to a small set of conditions, they can share information about evidence of objects in a scene. This shared evidence results in recognition performances that lie beyond the capacities of any of the currently used individual methods.

Visual Servoing Using Projective Kinematics

This paper presents a new approach to controlling a robot from video-feedback. In contrast to classical approaches, the entire system is modeled by means of projective geometry. For the robot’s geometry in particular, we introduce a new formalism “projective kinematics”. As a result, motions in joint- and image-space can be related without metric calibration, and a corresponding visual control law can be derived. We present experiments for projective robot calibration and visual servoing, both using uncalibrated cameras.