Laurent Lequievre

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Most of video-surveillance based applications use a foreground extraction algorithm to detect interest objects from videos provided by static cameras. This paper presents a benchmark dataset and evaluation process built from both synthetic and real videos, used in the BMC workshop (Background Models Challenge). This dataset focuses on outdoor situations(More)
In this paper, the control problem for a group of mobile robots keeping a geometric formation is considered. The proposed architecture of control allows to each robot to avoid obstacles and to rejoin the desired formation. To not complicate the control of such a system, it is proposed to divide the overall complex task into two basic tasks: attraction to a(More)
In this paper, we present a method to efficiently manage visual memory for autonomous vehicle navigation in large scale environments. It relies on two crucial issues for real-time navigation: an efficient organisation of the memory and small computational cost. A software platform (SoViN) dedicated to visual memory management and navigation strategies(More)
The field of in-hand robot manipulation of deformable objects is an open and key issue for the next-coming robots. Developing an adaptable and agile framework for the tasks where a robot grasps and manipulates different kinds of deformable objects, is a main goal in the literature. Many research works have been proposed to control the manipulation tasks(More)
Real-time accurate localization is a key component of an autonomous mobile robot. Visual localization algorithms usually rely on feature matching between the current view and a map using point descriptors. Many descriptors such as SIFT or SURF are designed to recognize features seen from different viewpoint. But in a robotic context, the robot movement can(More)
In this article, a localization system for a mobile robot, using a top-down multisensors approach and a map of the environment, is proposed. Popular methods try to optimize a global cost, track multihypothesis, or reduce the problem by using multisensors. These approaches are bottom-up: Each sensor data is analyzed even if it is not relevant [like a global(More)
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