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— In this paper, we present a novel benchmark for the evaluation of RGB-D SLAM systems. We recorded a large set of image sequences from a Microsoft Kinect with highly accurate and time-synchronized ground truth camera poses from a motion capture system. The sequences contain both the color and depth images in full sensor resolution (640 × 480) at video(More)
— We present an approach to simultaneous local-ization and mapping (SLAM) for RGB-D cameras like the Microsoft Kinect. Our system concurrently estimates the tra-jectory of a hand-held Kinect and generates a dense 3D model of the environment. We present the key features of our approach and evaluate its performance thoroughly on a recently published dataset,(More)
The practical applications of 3D model acquisition are manifold. In this paper, we present our RGB-D SLAM system, i.e., an approach to generate colored 3D models of objects and indoor scenes using the hand-held Microsoft Kinect sensor. Our approach consists of four processing steps as illustrated in Figure 1. First, we extract SURF features from the(More)
— The goal of our work is to provide a fast and accurate method to estimate the camera motion from RGB-D images. Our approach registers two consecutive RGB-D frames directly upon each other by minimizing the photometric error. We estimate the camera motion using non-linear minimization in combination with a coarse-to-fine scheme. To allow for noise and(More)
We present an energy-based approach to visual odometry from RGB-D images of a Microsoft Kinect camera. To this end we propose an energy function which aims at finding the best rigid body motion to map one RGB-D image into another one, assuming a static scene filmed by a moving camera. We then propose a linearization of the energy function which leads to a 6(More)
Robots operating in domestic environments generally need to interact with articulated objects, such as doors, cabinets, dishwashers or fridges. In this work, we present a novel, probabilistic framework for modeling articulated objects as kinematic graphs. Vertices in this graph correspond to object parts, while edges between them model their kinematic(More)
— In this paper, we propose a dense visual SLAM method for RGB-D cameras that minimizes both the photomet-ric and the depth error over all pixels. In contrast to sparse, feature-based methods, this allows us to better exploit the available information in the image data which leads to higher pose accuracy. Furthermore, we propose an entropy-based similarity(More)
— We provide a large dataset containing RGB-D image sequences and the ground-truth camera trajectories with the goal to establish a benchmark for the evaluation of visual SLAM systems. Our dataset contains the color and depth images of a Microsoft Kinect sensor and the ground-truth trajectory of camera poses. The data was recorded at full frame rate (30 Hz)(More)
—In this article we present a novel mapping system that robustly generates highly accurate 3D maps using an RGB-D camera. Our approach does not require any further sensors or odometry. With the availability of low-cost and lightweight RGB-D sensors such as the Microsoft Kinect, our approach applies to small domestic robots such as vacuum cleaners as well as(More)
— In this paper, we present a novel approach for identifying objects using touch sensors installed in the finger tips of a manipulation robot. Our approach operates on low-resolution intensity images that are obtained when the robot grasps an object. We apply a bag-of-words approach for object identification. By means of unsupervised clustering on training(More)