An integrated system for teaching new visually grounded words to a robot for non-expert users using a mobile device

International audienceIn this paper, we present a system allowing non- expert users to teach new words to their robot. In opposition to most of existing works in this area which focus on the associated visual perception and machine learning challenges, we choose to focus on the HRI challenges with the aim to show that it may improve the learning quality. We argue that by using mediator objects and in particular a handheld device, we can develop a human-robot interface which is not only intuitive and entertaining but will also "help" the user to provide "good" learning examples to the robot and thus will improve the efficiency of the whole learning system. The perceptual and machine learning parts of this system rely on an incremental version of visual bag-of-words. We also propose a system called ASMAT that makes it possible for the robot to incrementally build a model of a novel unknown object by simultaneously modelling and tracking it. We report experiments demonstrating the fast acquisition of robust object models using this approach

Une approche multi-agent pour la segmentation d'images de profondeur

National audienceDans cet article, nous présentons et nous évaluons une approche multi-agent pour la segmentation d’images de profondeur. L’approche consiste en l’utilisation d’une population d’agents autonomes pour la segmentation d’une image de profondeur en ses différentes régions planes. Les agents s’adaptent aux régions sur lesquelles ils se déplacent, puis effectuent des actions coopératives et compétitives produisant une segmentation collective de l’image. Un champ de potentiel artificiel est introduit afin de coordonner les mouvements des agents et de leur permettre de s’organiser autour des pixels d’intérêt. Les résultats expérimentaux obtenus par des images réelles montrent le potentiel de l’approche proposée pour l’analyse des images de profondeurs, et ce vis-à-vis de l’efficacité de segmentation et de la fiabilité des résultats

Graph-based segmentation and scene understanding for context-free point clouds

The acquisition of 3D point clouds representing the surface structure of real-world scenes has become common practice in many areas including architecture, cultural heritage and urban planning. Improvements in sample acquisition rates and precision are contributing to an increase in size and quality of point cloud data. The management of these large volumes of data is quickly becoming a challenge, leading to the design of algorithms intended to analyse and decrease the complexity of this data. Point cloud segmentation algorithms partition point clouds for better management, and scene understanding algorithms identify the components of a scene in the presence of considerable clutter and noise. In many cases, segmentation algorithms operate within the remit of a specific context, wherein their effectiveness is measured. Similarly, scene understanding algorithms depend on specific scene properties and fail to identify objects in a number of situations. This work addresses this lack of generality in current segmentation and scene understanding processes, and proposes methods for point clouds acquired using diverse scanning technologies in a wide spectrum of contexts. The approach to segmentation proposed by this work partitions a point cloud with minimal information, abstracting the data into a set of connected segment primitives to support efficient manipulation. A graph-based query mechanism is used to express further relations between segments and provide the building blocks for scene understanding. The presented method for scene understanding is agnostic of scene specific context and supports both supervised and unsupervised approaches. In the former, a graph-based object descriptor is derived from a training process and used in object identification. The latter approach applies pattern matching to identify regular structures. A novel external memory algorithm based on a hybrid spatial subdivision technique is introduced to handle very large point clouds and accelerate the computation of the k-nearest neighbour function. Segmentation has been successfully applied to extract segments representing geographic landmarks and architectural features from a variety of point clouds, whereas scene understanding has been successfully applied to indoor scenes on which other methods fail. The overall results demonstrate that the context-agnostic methods presented in this work can be successfully employed to manage the complexity of ever growing repositories

Range image segmentation using surface selection criterion

In this paper, we address the problem of recovering the true underlying model of a surface while performing the segmentation. First, and in order to solve the model selection problem, we introduce a novel criterion, which is based on minimising strain energy of fitted surfaces. We then evaluate its performance and compare it with many other existing model selection techniques. Using this criterion, we then present a robust range data segmentation algorithm capable of segmenting complex objects with planar and curved surfaces. The presented algorithm simultaneously identifies the type (order and geometric shape) of each surface and separates all the points that are part of that surface. This paper includes the segmentation results of a large collection of range images obtained from objects with planar and curved surfaces. The resulting segmentation algorithm successfully segments various possible types of curved objects. More importantly, the new technique is capable of detecting the association between separated parts of a surface, which has the same Cartesian equation while segmenting a scene. This aspect is very useful in some industrial applications of range data analysis

Range Image Segmentation Using Surface Selection Criterion

In this paper, we address the problem of recovering the true underlying model of a surface while performing the segmentation. First, and in order to solve the model selection problem, we introduce a novel criterion, which is based on minimising strain energy of fitted surfaces. We then evaluate its performance and compare it with many other existing model selection techniques. Using this criterion, we then present a robust range data segmentation algorithm capable of segmenting complex objects with planar and curved surfaces. The presented algorithm simultaneously identifies the type (order and geometric shape) of each surface and separates all the points that are part of that surface. This paper includes the segmentation results of a large collection of range images obtained from objects with planar and curved surfaces. The resulting segmentation algorithm successfully segments various possible types of curved objects. More importantly, the new technique is capable of detecting the association between separated parts of a surface, which has the same Cartesian equation while segmenting a scene. This aspect is very useful in some industrial applications of range data analysis

Range Image Segmentation Using Surface Selection Criterion

In this paper, we address the problem of recovering the true underlying model of a surface while performing the segmentation. First and in order to solve the model selection problem, we introduce a novel criterion which is based on minimising strain energy of fitted surfaces. We then evaluate its performance and compare it with many other existing model selection techniques. Using this criterion, we then present a robust range data segmentation algorithm capable of segmenting complex objects with planar and curved surfaces. The presented algorithm simultaneously identifies the type (order and geometric shape) of each surface and separates all the points that are part of that surface. The paper includes the segmentation results of a large collection of range images obtained from objects with planar and curved surfaces. The resulting segmentation algorithm successfully segments various possible types of curved objects. More importantly, the new technique is capable of detecting the association between separated parts of a surface, which has the same Cartesian equation while segmenting a scene. This aspect is very useful in some industrial applications of range data analysis