Jörg Stückler

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Building consistent models of objects and scenes from moving sensors is an important prerequisite for many recognition, manipulation, and navigation tasks. Our approach integrates color and depth measurements seamlessly in a multi-resolution map representation. We process image sequences from RGB-D cameras and consider their typical noise properties. In(More)
We propose a novel Large-Scale Direct SLAM algorithm for stereo cameras (Stereo LSD-SLAM) that runs in real-time at high frame rate on standard CPUs. In contrast to sparse interest-point based methods, our approach aligns images directly based on the photoconsistency of all high-contrast pixels, including corners, edges and high texture areas. It(More)
Pointing gestures are a common and intuitive way to draw somebody's attention to a certain object. While humans can easily interpret robot gestures, the perception of human behavior using robot sensors is more difficult. In this work, we propose a method for perceiving pointing gestures using a Time-of-Flight (ToF) camera. To determine the intended(More)
We present an efficient multi-resolution approach to segment a 3D point cloud into planar components. In order to gain efficiency, we process large point clouds iteratively from coarse to fine 3D resolutions: At each resolution, we rapidly extract surface normals to describe surface elements (surfels). We group surfels that cannot be associated with planes(More)
We propose a novel fast and robust method for obtaining 3D models with high-quality appearance using commodity RGB-D sensors. Our method uses a direct keyframebased SLAM frontend to consistently estimate the camera motion during the scan. The aligned images are fused into a volumetric truncated signed distance function representation, from which we extract(More)
Domestic service tasks require three main skills from autonomous robots: robust navigation, mobile manipulation, and intuitive communication with the users. Most robot platforms, however, support only one or two of the above skills. In this paper we present Dynamaid, a new robot platform for research on domestic service applications. For robust navigation,(More)
Grasping individual objects from an unordered pile in a box has been investigated in static scenarios so far. In this paper, we demonstrate bin picking with an anthropomorphic mobile robot. To this end, we extend global navigation techniques by precise local alignment with a transport box. Objects are detected in range images using a shape primitive-based(More)
We propose a real-time approach to learn semantic maps from moving RGB-D cameras. Our method models geometry, appearance, and semantic labeling of surfaces. We recover camera pose using simultaneous localization and mapping while concurrently recognizing and segmenting object classes in the images. Our object-class segmentation approach is based on random(More)
The mapping of environments is a prerequisite for many navigation and manipulation tasks. We propose a novel method for acquiring 3D maps of indoor scenes from a freely moving RGB-D camera. Our approach integrates color and depth cues seamlessly in a multi-resolution map representation. We consider measurement noise characteristics and exploit dense image(More)
For planning grasps and other object manipulation actions in complex environments, 3D semantic information becomes crucial. This paper focuses on the application of recent 3D Time-of-Flight (ToF) cameras in the context of semantic scene analysis. For being able to acquire semantic information from ToF camera data, we a) pre-process the data including(More)