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An Efficient Probabilistic 3D Mapping Framework Based on Octrees
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TLDR
In this paper, an open-source framework is presented to generate volumetric 3D environ- ment models based on octrees and uses probabilistic occupancy estimation, which explicitly repre- sents not only occupied space, but also free and unknown areas.Abstract:
Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environ- ment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly repre- sents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The re- sults demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.read more
Citations
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Book ChapterDOI
Active Recognition and Manipulation for Mobile Robot Bin Picking
Dirk Holz,Matthias Nieuwenhuisen,David Droeschel,Jörg Stückler,Alexander Berner,Jun Li,Reinhard Klein,Sven Behnke +7 more
TL;DR: A complete system including active object perception and grasp planning for bin picking with a mobile robot and the combination of shape and contour primitives makes this approach particularly robust even in the presence of noise, occlusions, and missing information.
Proceedings ArticleDOI
Multi-modal mapping and localization of unmanned aerial robots based on ultra-wideband and RGB-D sensing
TL;DR: A novel approach for environment mapping is introduced, exploiting the synergies between UWB sensors and point-clouds to produce a multi-modal 3D map that integrates the estimated UWB sensor position and is further integrated into a Monte Carlo Localization method to robustly estimate the UAV pose.
Journal ArticleDOI
Variable Resolution Occupancy Mapping Using Gaussian Mixture Models
TL;DR: The core contribution of this work is a memory-efficient method for deriving occupancy that is amenable to small or large corrections in pose without the need to regenerate the entire map.
Proceedings ArticleDOI
Visual industrial inspection using aerial robots
TL;DR: A UAV navigation system setup that uses visual-inertial sensor cues to estimate the UAV pose as well as to create a dense 3D map of the environment in real-time onboard the Uav, completely independent of GPS is presented.
Proceedings ArticleDOI
Learning high-dimensional Mixture Models for fast collision detection in Rapidly-Exploring Random Trees
Jinwook Huh,Daniel D. Lee +1 more
TL;DR: The proposed method is based upon Gaussian Mixture Models (GMM) that are learned using an incremental Expectation Maximization clustering algorithm trained online using exemplars provided by a slow, conventional kinematic-based collision detection routine.
References
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