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Robotic systems performing end-user oriented autonomous exploration can be deployed in different scenarios which not only require mapping but also simultaneous inspection of regions of interest for the end-user. In this work, we propose a novel Next-Best-View (NBV) planner which can perform full exploration and user-oriented exploration with inspection of the regions of interest using a mobile manipulator robot. We address the exploration-inspection problem as an instance of Multi-Objective Optimization (MOO) and propose a weighted-sum-based information gain function for computing NBVs for the RGB-D camera mounted on the arm. For both types of exploration tasks, we compare our approach with an existing state-of-the-art exploration method as the baseline and demonstrate our improvements in terms of total volume mapped and lower computational requirements. The real experiments with a mobile manipulator robot demonstrate the practicability and effectiveness of our approach outdoors.

Visual scene decomposition into semantic entities is one of the major challenges when creating a reliable object grasping system. Recently, we introduced a bottom-up hierarchical clustering approach which is able to segment objects and parts in a scene. In this paper, we introduce a transform from such a segmentation into a corresponding, hierarchical saliency function. In comprehensive experiments we demonstrate its ability to detect salient objects in a scene. Furthermore, this hierarchical saliency defines a most salient corresponding region (scale) for every point in an image. Based on this, an easy-to-use pick and place manipulation system was developed and tested exemplarily., Comment: Accepted for ICRA 2017

Visual grouping is a key mechanism in human scene perception. There, it belongs to the subconscious, early processing and is key prerequisite for other high level tasks such as recognition. In this paper, we introduce an efficient, realtime capable algorithm which likewise agglomerates a valuable hierarchical clustering of a scene, while using purely local appearance statistics. To speed up the processing, first we subdivide the image into meaningful, atomic segments using a fast Watershed transform. Starting from there, our rapid, agglomerative clustering algorithm prunes and maintains the connectivity graph between clusters to contain only such pairs, which directly touch in the image domain and are reciprocal nearest neighbors (RNN) wrt. a distance metric. The core of this approach is our novel cluster distance: it combines boundary and surface statistics both in terms of appearance as well as spatial linkage. This yields state-of-the-art performance, as we demonstrate in conclusive experiments conducted on BSDS500 and Pascal-Context datasets., Comment: Asian Conf. on Computer Vision (ACCV) 2016