El Mustapha Mouaddib

Bio: El Mustapha Mouaddib is an academic researcher from University of Picardie Jules Verne. The author has contributed to research in topics: Mobile robot & Catadioptric system. The author has an hindex of 20, co-authored 113 publications receiving 1834 citations. Previous affiliations of El Mustapha Mouaddib include University of Girona.

Recent progress in structured light in order to solve the correspondence problem in stereovision

Abstract: We present a summary of the most significant techniques, used in the last few years, concerning the coded structured light methods employed to get 3D information. In fact, depth perception is one of the most important subjects in computer vision. Stereovision is an attractive and widely used method, but, rather limited to make 3D surface maps, due to the correspondence problem. The correspondence problem can be improved using a method based on a structured light concept, projecting a given pattern on the measuring surfaces, although some relations between the projected pattern and the reflected one must be solved. This relationship can be directly found codifying the projected light, so that, each imaged region of the projected pattern carries the necessary information to solve the correspondence problem. We do not need to mention the numerous advantages in accurate obtention of 3D information for many research subjects, such as: robotics, autonomous navigation, shape analysis, and so on.

A mobile robot using a panoramic view

Abstract: Mobile robots use actually a combination of internal and external sensors to determine their position and orientation in a path following period. Incremental encoders, gyrometers are generally used to give an approximative estimation of localization. Nevertheless, the cumulative drifts of these internal sensors must be periodically corrected with an exteroceptive sensor. So, the authors present, in this paper, an optical omnidirectional sensor which can give, with an adapted software, an absolute localization. This sensor is made of a CCD video camera associated with a conic shaped reflector; so, a view of a 2 /spl pi/ radian field is available to compute the position of the robot. The authors report the matching algorithm allowing a local observed scene to be replaced in the global navigation area. The authors conduct the accuracy analysis of this global positioning system, and present the experimental results.

High-accuracy stereo depth maps using structured light

Abstract: Progress in stereo algorithm performance is quickly outpacing the ability of existing stereo data sets to discriminate among the best-performing algorithms, motivating the need for more challenging scenes with accurate ground truth information. This paper describes a method for acquiring high-complexity stereo image pairs with pixel-accurate correspondence information using structured light. Unlike traditional range-sensing approaches, our method does not require the calibration of the light sources and yields registered disparity maps between all pairs of cameras and illumination projectors. We present new stereo data sets acquired with our method and demonstrate their suitability for stereo algorithm evaluation. Our results are available at http://www.middlebury.edu/stereo/.

Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection

Abstract: We describe a learning-based approach to hand-eye coordination for robotic grasping from monocular images. To learn hand-eye coordination for grasping, we trained a large convolutional neural netwo...

High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth

Abstract: We present a structured lighting system for creating high-resolution stereo datasets of static indoor scenes with highly accurate ground-truth disparities. The system includes novel techniques for efficient 2D subpixel correspondence search and self-calibration of cameras and projectors with modeling of lens distortion. Combining disparity estimates from multiple projector positions we are able to achieve a disparity accuracy of 0.2 pixels on most observed surfaces, including in half-occluded regions. We contribute 33 new 6-megapixel datasets obtained with our system and demonstrate that they present new challenges for the next generation of stereo algorithms.

Combination of Evidence in Dempster-Shafer Theory

Abstract: Dempster-Shafer theory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty. The significant innovation of this framework is that it allows for the allocation of a probability mass to sets or intervals. Dempster-Shafer theory does not require an assumption regarding the probability of the individual constituents of the set or interval. This is a potentially valuable tool for the evaluation of risk and reliability in engineering applications when it is not possible to obtain a precise measurement from experiments, or when knowledge is obtained from expert elicitation. An important aspect of this theory is the combination of evidence obtained from multiple sources and the modeling of conflict between them. This report surveys a number of possible combination rules for Dempster-Shafer structures and provides examples of the implementation of these rules for discrete and interval-valued data.