We present a novel method based on saliency and segmentation to generate generic object candidates from RGB-D data. Our method uses saliency as a cue to roughly estimate the location and extent of the objects present in the scene. Salient regions are used to glue together the segments obtained from over-segmenting the scene by either color or depth segmentation algorithms, or by a combination of both. We suggest a late-fusion approach that first extracts segments from color and depth independently before fusing them to exploit that the data is complementary. Furthermore, we investigate several mechanisms for ranking the object candidates. We evaluate on one publicly available dataset and on one challenging sequence with a high degree of clutter. The results show that we are able to retrieve most objects in real-world indoor scenes and clearly outperform other state-of-the art methods.

Attention is a concept of human perception that enables human subjects to select the potentially relevant parts out of the huge amount of sensory data and that enables interactions with other human subjects by sharing attention with each other. These abilities are also of large interest for autonomous robots, therefore, interest in modeling concepts of human attention computationally has increased strongly in the robotics community during the last decade. Especially in human-robot interaction, the ability to detect what a human partner is attending to and to act in a similar way to enable intuitive communication, are important skills for a robotic system.

The task of searching and grasping objects in cluttered scenes, typical of robotic applications in domestic environments requires fast object detection and segmentation. Attentional mechanisms provide a means to detect and prioritize processing of objects of interest. In this work, we combine a saliency operator based on symmetry with a segmentation method based on clustering locally planar surface patches, both operating on 2.5D point clouds (RGB-D images) as input data to yield a novel approach to table-top scene segmentation. Evaluation on indoor table-top scenes containing man-made objects clustered in piles and dumped in a box show that our approach to selection of attention points significantly improves performance of state-of-the-art attention-based segmentation methods.

Generation of grasps for automated object manipulation in cluttered scenarios presents major challenges for various modules of the pipeline such as 2D/3D visual processing, 3D modeling, grasp hypothesis generation, grasp planning and path planning. In this paper, we present a solution framework for solving a complex instance of the problem - represented by a heap of unknown and unstructured objects in a bounded environment - in our case, a box; with the goal being removing all objects in the box using an attention driven object modeling approach to cognitive grasp planning. The focus of the algorithm delves on Grasping by Components (GBC), with a prioritization scheme derived from scene based attention and attention driven segmentation. In order to overcome the traditional challenge of segmentation performing poorly in cluttered scenes, we employ a novel active segmentation approach suited to our scenario. While the attention module helps prioritize objects in the heap and salient regions, the GBC scheme segments out parts and generates grasp hypotheses for each part. GBC is a very important component of any scalable and holistic grasping system since it abstracts point cloud object data with parametric shapes and no apriori knowledge (such as 3D models) is required. Earlier work in 3D model building (such as CAD based, simple geometries, bounding boxes, Superquadrics etc.) have depended on precise shape and pose recognition as well as exhaustive training to learn or exhaustive searching in grasp space to generate good grasp hypotheses. These methods are not scalable for real-time scenarios, complex shapes and unknown environments - key challenges in robotic grasping. In order to alleviate this concern, we present a novel parametric algorithm to estimate grasp points and approach vectors from the 3D parametric shape model, along with innovative schemes to optimize the computation of the parametric models as well as to refine the generated grasp hypotheses based on the scene information to aid path planning. We present evaluation of our complex grasping pipeline for cluttered heaps through a series of test sequences involving removal of objects from a box, along with evaluations for our attention mechanisms, active segmentation, 3D model fitting optimizations and quality of our grasp hypotheses.

Implementing information management system is still a challenging task for any organization. A lot of surveys are conducted to find out the root causes of implementation problems and propose new methods that can improve and simplify this process. In this paper we suggest using an ontology-based decision support system to avoid some implementation challenges.