• Corpus ID: 207887346

6D Visual SLAM for RGB-D Sensors

  title={6D Visual SLAM for RGB-D Sensors},
  author={J{\"u}rgen Hess and Nikolas Engelhard and J{\"u}rgen Sturm},
Zusammenfassung Zur Automatisierung komplexer Manipulationsaufgaben in dynamischen oder unbekannten Umgebungen benötigt die Steuerungssoftware eines autonomen Roboters eine Repräsentation des Arbeitsbereiches, mit der die Kollisionsfreiheit bei der Durchführung der Aufgabe gewährleistet werden kann. Dieser Beitrag beschreibt ein neues System zur Erstellung von 3D-Umgebungsrepräsentationen aus den RGB-D-Daten neuartiger Kameras, wie der Microsoft Kinect. Durch die Unabhängigkeit von weiterer… 
D Mapping with an RGB-D Camera
A novel mapping system that robustly generates highly accurate 3D maps using an RGB-D camera that applies to small domestic robots as well as flying robots such as quadrocopters and free-hand reconstruction of detailed 3D models.
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.
A Fast Robot Identification and Mapping Algorithm Based on Kinect Sensor
The reduced dynamic covariance scaling (DCS) algorithm is formulated as a global optimization problem under the G2O framework to improve the accuracy of SLAM and achieves higher processing speed and better accuracy.
Integrating depth and color cues for dense multi-resolution scene mapping using RGB-D cameras
  • J. Stückler, Sven Behnke
  • Computer Science
    2012 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)
  • 2012
This work proposes a novel method for acquiring 3D maps of indoor scenes from a freely moving RGB-D camera that integrates color and depth cues seamlessly in a multi-resolution map representation and proposes an efficient randomized loop-closure technique that is designed for on-line operation.
Multi-resolution surfel maps for efficient dense 3D modeling and tracking
Color smoothing for RGB-D data using entropy information


RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments
This paper presents RGB-D Mapping, a full 3D mapping system that utilizes a novel joint optimization algorithm combining visual features and shape-based alignment to achieve globally consistent maps.
Realtime Visual and Point Cloud SLAM Nicola Fioraio Willow Garage
This paper presents a technique that performs generalized ICP on two frames in typical times of 10 ms, using an efficient bundle-adjustment framework, so that it can combine ICP with visual feature matches, both for frame-frame matching, and overall global adjustment.
Towards a benchmark for RGB-D SLAM evaluation
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.
Efficient estimation of accurate maximum likelihood maps in 3D
This paper presents an efficient solution to the SLAM problem that is able to distribute a rotational error over a sequence of nodes and applies a variant of gradient descent to solve the error minimization problem.
Scale Drift-Aware Large Scale Monocular SLAM
This paper describes a new near real-time visual SLAM system which adopts the continuous keyframe optimisation approach of the best current stereo systems, but accounts for the additional challenges presented by monocular input and presents a new pose-graph optimisation technique which allows for the efficient correction of rotation, translation and scale drift at loop closures.
Real-time simultaneous localisation and mapping with a single camera
  • A. Davison
  • Computer Science
    Proceedings Ninth IEEE International Conference on Computer Vision
  • 2003
This work presents a top-down Bayesian framework for single-camera localisation via mapping of a sparse set of natural features using motion modelling and an information-guided active measurement strategy, in particular addressing the difficult issue of real-time feature initialisation via a factored sampling approach.
On measuring the accuracy of SLAM algorithms
A framework for analyzing the results of a SLAM approach based on a metric for measuring the error of the corrected trajectory is proposed, which overcomes serious shortcomings of approaches using a global reference frame to compute the error.
Parallel Tracking and Mapping for Small AR Workspaces
A system specifically designed to track a hand-held camera in a small AR workspace, processed in parallel threads on a dual-core computer, that produces detailed maps with thousands of landmarks which can be tracked at frame-rate with accuracy and robustness rivalling that of state-of-the-art model-based systems.
Humanoid robot localization in complex indoor environments
A localization method for humanoid robots navigating in arbitrary complex indoor environments using only onboard sensing to globally determine and track a humanoid's 6D pose in a 3D world model, which may contain multiple levels connected by staircases.
G2o: A general framework for graph optimization
G2o, an open-source C++ framework for optimizing graph-based nonlinear error functions, is presented and demonstrated that while being general g2o offers a performance comparable to implementations of state-of-the-art approaches for the specific problems.