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A benchmark for the evaluation of RGB-D SLAM systems
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 is recorded for the evaluation of RGB-D SLAM systems.
An evaluation of the RGB-D SLAM system
We present an approach to simultaneous localization and mapping (SLAM) for RGB-D cameras like the Microsoft Kinect. Our system concurrently estimates the trajectory of a hand-held Kinect and
3-D Mapping With an RGB-D Camera
A novel mapping system that robustly generates highly accurate 3-D maps using an RGB-D camera that applies to small domestic robots such as vacuum cleaners, as well as flying robotssuch as quadrocopters.
D Mapping with an RGB-D Camera
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
Unsupervised discovery of object classes from range data using latent Dirichlet allocation
Practical experiments demonstrate, that the approach is able to learn object class models autonomously that are consistent with the true classifications provided by a human, and furthermore outperforms unsupervised method such as hierarchical clustering that operate on a distance metric.
Real-time 3 D visual SLAM with a hand-held RGB-D camera
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
Monocular range sensing: A non-parametric learning approach
A novel approach to learning the relationship between range measurements and visual features extracted from a single monocular camera image is presented, using Gaussian processes, a non-parametric learning technique that not only yields the most likely range prediction corresponding to a certain visual input but also the predictive uncertainty.
Learning the dynamics of doors for robotic manipulation
This paper presents an approach to learn a dynamic model of a door from sensor observations and utilize it for effectively swinging the door open to a desired angle and applies Gaussian process regression to learn the deceleration of the door with respect to position and velocity.
A nonparametric learning approach to range sensing from omnidirectional vision
It is shown in this paper how an omnidirectional camera can be used as an alternative to such range sensors and is able to estimate range with an accuracy comparable to that of dedicated sensors based on sonar or infrared light.