Michel Dhome

Determination of the attitude of 3D objects from a single perspective view

A method for finding analytical solutions to the problem of determining the attitude of a 3D object in space from a single perspective image is presented. Its principle is based on the interpretation of a triplet of any image lines as the perspective projection of a triplet of linear ridges of the object model, and on the search for the model attitude consistent with these projections. The geometrical transformations to be applied to the model to bring it into the corresponding location are obtained by the resolution of an eight-degree equation in the general case. Using simple logical rules, it is shown on examples related to polyhedra that this approach leads to results useful for both location and recognition of 3D objects because few admissible hypotheses are retained from the interpolation of the three line segments. Line matching by the prediction-verification procedure is thus less complex. >

Real Time Localization and 3D Reconstruction

In this paper we describe a method that estimates the motion of a calibrated camera (settled on an experimental vehicle) and the tridimensional geometry of the environment. The only data used is a video input. In fact, interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key-frames are selected and permit the features 3D reconstruction. The algorithm is particularly appropriate to the reconstruction of long images sequences thanks to the introduction of a fast and local bundle adjustment method that ensures both good accuracy and consistency of the estimated camera poses along the sequence. It also largely reduces computational complexity compared to a global bundle adjustment. Experiments on real data were carried out to evaluate speed and robustness of the method for a sequence of about one kilometer long. Results are also compared to the ground truth measured with a differential GPS.

Monocular Vision for Mobile Robot Localization and Autonomous Navigation

This paper presents a new real-time localization system for a mobile robot. We show that autonomous navigation is possible in outdoor situation with the use of a single camera and natural landmarks. To do that, we use a three step approach. In a learning step, the robot is manually guided on a path and a video sequence is recorded with a front looking camera. Then a structure from motion algorithm is used to build a 3D map from this learning sequence. Finally in the navigation step, the robot uses this map to compute its localization in real-time and it follows the learning path or a slightly different path if desired. The vision algorithms used for map building and localization are first detailed. Then a large part of the paper is dedicated to the experimental evaluation of the accuracy and robustness of our algorithms based on experimental data collected during two years in various environments.

Hyperplane approximation for template matching

Hager and Belhumeur (1998) proposed a general framework for object tracking in video images. It consists of low-order parametric models for the image motion of a target region. These models are used to predict movement and to track the target. The difference in intensity between the pixels belonging to the current region and the pixels of the selected target (learned during an offline stage) allows a straightforward prediction of the region position in the current image. The main aim of the article is to propose an important improvement within this framework, making the convergence faster with the same amount of online computation.

Generic and real-time structure from motion using local bundle adjustment

This paper describes a method for estimating the motion of a calibrated camera and the three-dimensional geometry of the filmed environment. The only data used is video input. Interest points are tracked and matched between frames at video rate. Robust estimates of the camera motion are computed in real-time, key frames are selected to enable 3D reconstruction of the features. We introduce a local bundle adjustment allowing 3D points and camera poses to be refined simultaneously through the sequence. This significantly reduces computational complexity when compared with global bundle adjustment. This method is applied initially to a perspective camera model, then extended to a generic camera model to describe most existing kinds of cameras. Experiments performed using real-world data provide evaluations of the speed and robustness of the method. Results are compared to the ground truth measured with a differential GPS. The generalized method is also evaluated experimentally, using three types of calibrated cameras: stereo rig, perspective and catadioptric.

Do We Really Need an Accurate Calibration Pattern to Achieve a Reliable Camera Calibration

This article raises the problem of errors caused by the metrology of a calibration pattern to the accurate estimation of the intrinsic and extrinsic calibration parameters modeling the video system. In order to take into account these errors a new approach is proposed that enables us to compute in the same time the traditional calibration parameters and the 3D geometry of the calibration pattern using a multi-images calibration algorithm. Experimental results shows that the proposed algorithm leads to reliable calibration results and proves that calibration errors no longer depend on the accuracy of calibration point measurement, but on the accuracy of calibration point detection in the image plane.

Real Time Robust Template Matching

One of the most popular methods to extract useful informations from an image sequence is the template matching approach. In this well known method the tracking of a certain feature or target over time is based on the comparison of the content of each image with a sample template. In this article, we propose an efficient robust template matching algorithm that is able to track targets in real time. Special attention is paid to occlusions handling and illumination variations.

Outdoor autonomous navigation using monocular vision

In this paper, a complete system for outdoor robot navigation is presented. It uses only monocular vision. The robot is first guided on a path by a human. During this learning step, the robot records a video sequence. From this sequence, a three dimensional map of the trajectory and the environment is built. When this map has been computed, the robot is able to follow the same trajectory by itself. Experimental results carried out with an urban electric vehicle are shown and compared to the ground truth.

Simplifying the kinematic calibration of parallel mechanisms using vision-based metrology

In this paper, a vision-based measuring device is proposed and experimentally demonstrated to be an accurate, flexible, and low-cost tool for the kinematic calibration of parallel mechanisms. The accuracy and ease of use of the proposed vision sensor are outlined, with the suppression of the need for an accurate calibration target, and adequacy to the kinematic calibration process is investigated. In particular, identifiability conditions with the use of such an exteroceptive sensor are derived, considering the calibration with inverse or implicit models. Extensive results are given, with the evaluation of the measuring device and the calibration of an H4 robot. Using the full-pose measurement, an experimental analysis of the optimal calibration model is achieved, with study of the kinematic behavior of the mechanism. The efficiency of the provided method is thus evaluated, and the applicability of vision-based measuring devices to the context of kinematic calibration of parallel mechanisms is discussed.

Three-dimensional reconstruction by zooming

It is shown that it is possible to infer 3-D information from a set of images taken with a zoom lens. A precise study of the optical properties of such a lens gives two major results: The intersection between the optical axis and the image plane can be independently and accurately determined, and the pin-hole model cannot be used directly for the approximation of such a complex lens system. To explain the optical phenomena occurring during a zoom-lens focal-length change, it is shown that a thick optical model must be considered. Experimental reconstruction results for a set of real images, are given. >