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A benchmark for the evaluation of RGB-D SLAM systems
TLDR
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
Towards a benchmark for RGB-D SLAM evaluation
TLDR
A large dataset containing RGB-D image sequences and the ground-truth camera trajectories is provided and an evaluation criterion for measuring the quality of the estimated camera trajectory of visual SLAM systems is proposed.
Real-time 3 D visual SLAM with a hand-held RGB-D camera
TLDR
This paper presents the RGB-D SLAM system, an approach to generate colored 3D models of objects and indoor scenes using the hand-held Microsoft Kinect sensor, and applies SURF instead of SIFT features.
Nonparametric Bayesian Models for Unsupervised Scene Analysis and Reconstruction
TLDR
A novel hierarchical generative model to reason about latent object constellations in a scene, a combination of Dirichlet processes and beta processes, which allow for a probabilistic treatment of the unknown dimensionality of the parameter space is proposed.
A Bayesian Approach to Learning 3D Representations of Dynamic Environments
TLDR
The problem of detecting occurrences of non-stationary objects in range readings can be solved online under the assumption of a consistent Bayesian framework and all parameters involved in the detection process obey a clean probabilistic interpretation.
6D Visual SLAM for RGB-D Sensors
Zusammenfassung Zur Automatisierung komplexer Manipulationsaufgaben in dynamischen oder unbekannten Umgebungen benötigt die Steuerungssoftware eines autonomen Roboters eine Repräsentation des
Unsupervised Scene Analysis Using Semiparametric Bayesian Models
TLDR
A novel hierarchical generative model is proposed to infer the latent groups of objects in a scene using Markov chain Monte Carlo techniques for inference and experiments with simulated as well as real-world data obtained from a Kinect RGB-D camera.
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