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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.
View on IEEE
Probabilistic Robotics (Intelligent Robotics and Autonomous Agents)
OctoMap: an efficient probabilistic 3D mapping framework based on octrees
TLDR
An open-source framework to generate volumetric 3D environment models based on octrees and uses probabilistic occupancy estimation that represents not only occupied space, but also free and unknown areas and an octree map compression method that keeps the 3D models compact.
View on Springer
The dynamic window approach to collision avoidance
TLDR
This approach, designed for mobile robots equipped with synchro-drives, is derived directly from the motion dynamics of the robot and safely controlled the mobile robot RHINO in populated and dynamic environments.
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Deep learning with convolutional neural networks for EEG decoding and visualization
TLDR
This study shows how to design and train convolutional neural networks to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping.
View on Wolters Kluwer
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
Recently, Rao-Blackwellized particle filters (RBPF) have been introduced as an effective means to solve the simultaneous localization and mapping problem. This approach uses a particle filter in…
View on IEEE
G2o: A general framework for graph optimization
TLDR
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.
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Robust Monte Carlo localization for mobile robots
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Monte Carlo localization for mobile robots
TLDR
The Monte Carlo localization method is introduced, where the probability density is represented by maintaining a set of samples that are randomly drawn from it, and it is shown that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
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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…
View on IEEE
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