S. Dupitier

Bio: S. Dupitier is an academic researcher from Centre national de la recherche scientifique. The author has contributed to research in topics: 3D single-object recognition & Context (language use). The author has an hindex of 1, co-authored 1 publications receiving 7 citations.

3D object recognition using spin-images for a humanoid stereoscopic vision system

Abstract: This paper presents a 3D object recognition method based on spin-images for a humanoid robot having a stereoscopic vision system. Spin-images have been proposed to search CAD models database, and use 3D range informations. In this context, the use of a vision system is taken into account through a multi-resolution approach. A method for quickly computing multi-resolution and interpolating spin-images is proposed. The results on simulation and on real data are given, and show the effectiveness of this method.

3D object recognition and classification: a systematic literature review

Abstract: In this paper, we present a systematic literature review concerning 3D object recognition and classification. We cover articles published between 2006 and 2016 available in three scientific databases (ScienceDirect, IEEE Xplore and ACM), using the methodology for systematic review proposed by Kitchenham. Based on this methodology, we used tags and exclusion criteria to select papers about the topic under study. After the works selection, we applied a categorization process aiming to group similar object representation types, analyzing the steps applied for object recognition, the tests and evaluation performed and the databases used. Lastly, we compressed all the obtained information in a general overview and presented future prospects for the area.

Integrating walking and vision to increase humanoid autonomy

Abstract: Aiming at building versatile humanoid systems, we present in this paper the real-time implementation of behaviors which integrate walking and vision to achieve general functionalities. The paper describes how real-time — or high-bandwidth — cognitive processes can be obtained by combining vision with walking. The central point of our methodology is to use appropriate models to reduce the complexity of the search space. We will describe the models introduced in the different blocks of the system and their relationships: walking pattern, self-localization and map building, real-time reactive vision behaviors, and planning.

Towards shape-based visual object categorization for humanoid robots

Abstract: Humanoid robots should be able to grasp and handle objects in the environment, even if the objects are seen for the first time. A plausible solution to this problem is to categorize these objects into existing classes with associated actions and functional knowledge. So far, efforts on visual object categorization using humanoid robots have either been focused on appearance-based methods or have been restricted to object recognition without generalization capabilities. In this work, a shape model-based approach using stereo vision and machine learning for object categorization is introduced. The state-of-the-art features for shape matching and shape retrieval were evaluated and selectively transfered into the visual categorization. Visual sensing from different vantage points allows the reconstruction of 3D mesh models of the objects found in the scene by exploiting knowledge about the environment for model-based segmentation and registration. These reconstructed 3D mesh models were used for shape feature extraction for categorization and provide sufficient information for grasping and manipulation. Finally, the visual categorization was successfully performed with a variety of features and classifiers allowing proper categorization of unknown objects even when object appearance and shape substantially differ from the training set. Experimental evaluation with the humanoid robot ARMAR-IIIa is presented.

Treasure hunting for humanoids robot

Abstract: This paper intends to describe the current status of our group in trying to make a humanoid robot autonomously build an internal representation of an object, and later on to find it in an unknown environment. This problem is named "treasure hunting". In both cases, the main difficulty is to be able to find the next best position of the vision sensor in order to realize the behavior while taking care of the robots limitation. We briefly describe the models and the processes we are currently investigating in building this overall behavior. Along the description we stress the current key problems faced while trying to solve this problem.

Vision based motion generation for humanoid robots

Abstract: This manuscript present my research activities on real-time vision-based behaviors for complex robots such as humanoids. The underlying main scientific question structuring this work is the following: "What are the decisional processes which make possible for a humanoid robot to generate motion in real-time based upon visual information ?" In soccer humans can decide to kick a ball while running and when all the other players are constantly moving. When recast as an optimization problem for a humanoid robot, finding a solution for such behavior is generally computationally hard. For instance, the problem of visual search consider in this work is NP-complete. The first part of this work is concerned about real-time motion generation. Starting from the general constraints that a humanoid robot has to fulfill to generate a feasible motion, some core problems are presented. From this several contributions allowing a humanoid robot to react to change in the environment are presented. They revolve around walking pattern generation, whole body motion for obstacle avoidance, and real-time foot-step planning in constrained environment. The second part of this work is concerned about real-time acquisition of knowledge on the environment through computer vision. Two main behaviors are considered: visual-search and visual object model construction. They are considered as a whole taking into account the model of the sensor, the motion cost, the mechanical constraints of the robot, the geometry of the environment as well as the limitation of the vision processes. In addition contributions on coupling Self Localization and Map Building with walking, real-time foot-steps generation based on visual servoing are presented. Finally the core technologies developed in the previous contexts were used in different applications: Human-Robot interaction, tele-operation, human behavior analysis. Based upon the feedback of several integrated demonstrators on the humanoid robot HRP-2, the last part of this thesis tries to draw some directions where innovative ideas may break some current technical locks in humanoid robotics.