octomap.github.io

OctoMap - 3D occupancy mapping

An Efficient Probabilistic 3D Mapping Framework Based on Octrees. Implements a 3D occupancy grid mapping approach, providing data structures and mapping algorithms in C particularly suited for robotics. The map implementation is based on an octree and is designed to meet the following requirements:. It is possible to add new information or sensor readings at any time. Modeling and updating is done in a probabilistic. The extent of the map does not have to be known in advance. Instead, the map is dyna...

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OctoMap - 3D occupancy mapping | octomap.github.io Reviews

<META> DESCRIPTION

An Efficient Probabilistic 3D Mapping Framework Based on Octrees. Implements a 3D occupancy grid mapping approach, providing data structures and mapping algorithms in C particularly suited for robotics. The map implementation is based on an octree and is designed to meet the following requirements:. It is possible to add new information or sensor readings at any time. Modeling and updating is done in a probabilistic. The extent of the map does not have to be known in advance. Instead, the map is dyna...

<META> KEYWORDS

1 octomap

2 the octomap library

3 full 3d model

4 updatable

5 flexible

6 compact

7 and armin hornung

8 for their contributions

9 overview

10 octomap github repository

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octomap,the octomap library,full 3d model,updatable,flexible,compact,and armin hornung,for their contributions,overview,octomap github repository,download source packages,installation instructions,octomap in ros,mailing list,issue tracker,example projects

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OctoMap - 3D occupancy mapping | octomap.github.io Reviews

https://octomap.github.io

An Efficient Probabilistic 3D Mapping Framework Based on Octrees. Implements a 3D occupancy grid mapping approach, providing data structures and mapping algorithms in C particularly suited for robotics. The map implementation is based on an octree and is designed to meet the following requirements:. It is possible to add new information or sensor readings at any time. Modeling and updating is done in a probabilistic. The extent of the map does not have to be known in advance. Instead, the map is dyna...

LINKS TO THIS WEBSITE

arminhornung.de

www.ArminHornung.de - Die Homepage von Armin Hornung: Code

http://www.arminhornung.de/Code_de

Die private Homepage von Armin Hornung. You can find most of the code that I develop and maintain on github.com/ahornung. See below for a short overview of some larger projects. 3D Mapping with OctoMap. Provides a compact and efficient C data structure for mapping 3D environments, e.g. with mobile robots. I am the co-developer and current maintainer of the project, which is available open-source at GitHub. Under the BSD license. See octomap.github.io. Humanoid Robot Navigation for ROS. Zuletzt geänd...

arminhornung.de

www.ArminHornung.de - Die Homepage von Armin Hornung: Code

http://www.arminhornung.de/Code

Die private Homepage von Armin Hornung. You can find most of the code that I develop and maintain on github.com/ahornung. See below for a short overview of some larger projects. 3D Mapping with OctoMap. Provides a compact and efficient C data structure for mapping 3D environments, e.g. with mobile robots. I am the co-developer and current maintainer of the project, which is available open-source at GitHub. Under the BSD license. See octomap.github.io. Humanoid Robot Navigation for ROS. Zuletzt geänd...

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Nathan's Lucubrations; on computers, music and the outdoors

http://hardcorehackers.com/cgi-bin/blosxom

Nathan's Lucubrations; on computers, music and the outdoors. Thu, 21 Jan 2016. Day Hike: Black mountain from Garlock road. I hiked Black mountain, this time from the Garlock road side. Participants: Nathan Paul Simons, Stephen Verbovszky, Randy Turner. Weather notes: a little overcast, not cold. Success/failure: everyone made the peak. Vehicle mileage: 56km 54km = 110km. Hiking mileage: 8.3km 9.6km = 17.9km. Elevation gain: 856m 435m = 1291m. Drive time to: 1h20m. Drive time from: 1h5m. Thu, 19 Nov 2015.

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MechLab Engineering - Virtual Lidar Test Bench

http://mechlab-engineering.de/references/virtual-lidar-test-bench

Virtual Lidar Test Bench. Sie befinden sich hier: :. Virtual Lidar Test Bench. Virtual Lidar Test Bench. Zur Algorithmenentwicklung oder -absicherung ist es komfortabel, wenn das gewünschte Resultat (das „Soll“) bekannt ist. Bei der Messung von realen Situationen ergeben sich eine Vielzahl von kleinen, untergeordneten Nebeneffekten, welche das Ergebnis beeinflussen oder verfälschen. Für viele Anwendungsfälle ist dieser hohe Detailgrad nicht nötig, oftmals nicht gewünscht. Die virtuelle Messung erfolgt du...

hrl.informatik.uni-freiburg.de

Teaching - WS 2011/12 - Humanoid Robots Lab

http://hrl.informatik.uni-freiburg.de/teaching/ws11

Imitation of Human Whole-Body Motions. Autonomous Navigation Based on Depth Camera Data. Climbing Complex Staircases and Traversing Ramps. Efficient Vision-based Obstacle Avoidance. Efficient Path Planning for Humanoids. 6D Robot Localization in Complex Indoor Environments. Learning Reliable and Efficient Navigation with a Humanoid. Metric Localization with Scale-Invariant Visual Features using Monocular Vision. Learning Efficient Policies for Vision-based Navigation. Learning Motion Patterns of People.

hrl.informatik.uni-freiburg.de

Teaching - Humanoid Robots Lab

http://hrl.informatik.uni-freiburg.de/teaching

Imitation of Human Whole-Body Motions. Autonomous Navigation Based on Depth Camera Data. Climbing Complex Staircases and Traversing Ramps. Efficient Vision-based Obstacle Avoidance. Efficient Path Planning for Humanoids. 6D Robot Localization in Complex Indoor Environments. Learning Reliable and Efficient Navigation with a Humanoid. Metric Localization with Scale-Invariant Visual Features using Monocular Vision. Learning Efficient Policies for Vision-based Navigation. Learning Motion Patterns of People.

hrl.informatik.uni-freiburg.de

Publications 2012 - Humanoid Robots Lab

http://hrl.informatik.uni-freiburg.de/publications/2012

Imitation of Human Whole-Body Motions. Autonomous Navigation Based on Depth Camera Data. Climbing Complex Staircases and Traversing Ramps. Efficient Vision-based Obstacle Avoidance. Efficient Path Planning for Humanoids. 6D Robot Localization in Complex Indoor Environments. Learning Reliable and Efficient Navigation with a Humanoid. Metric Localization with Scale-Invariant Visual Features using Monocular Vision. Learning Efficient Policies for Vision-based Navigation. Learning Motion Patterns of People.

hrl.informatik.uni-freiburg.de

Teaching - WS 2012/13 - Humanoid Robots Lab

http://hrl.informatik.uni-freiburg.de/teaching/ws12

Imitation of Human Whole-Body Motions. Autonomous Navigation Based on Depth Camera Data. Climbing Complex Staircases and Traversing Ramps. Efficient Vision-based Obstacle Avoidance. Efficient Path Planning for Humanoids. 6D Robot Localization in Complex Indoor Environments. Learning Reliable and Efficient Navigation with a Humanoid. Metric Localization with Scale-Invariant Visual Features using Monocular Vision. Learning Efficient Policies for Vision-based Navigation. Learning Motion Patterns of People.

hrl.informatik.uni-freiburg.de

Systeme I WS 2013/14 - Humanoid Robots Lab

http://hrl.informatik.uni-freiburg.de/teaching/ws13/systeme

Imitation of Human Whole-Body Motions. Autonomous Navigation Based on Depth Camera Data. Climbing Complex Staircases and Traversing Ramps. Efficient Vision-based Obstacle Avoidance. Efficient Path Planning for Humanoids. 6D Robot Localization in Complex Indoor Environments. Learning Reliable and Efficient Navigation with a Humanoid. Metric Localization with Scale-Invariant Visual Features using Monocular Vision. Learning Efficient Policies for Vision-based Navigation. Learning Motion Patterns of People.

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An Efficient Probabilistic 3D Mapping Framework Based on Octrees. Implements a 3D occupancy grid mapping approach, providing data structures and mapping algorithms in C particularly suited for robotics. The map implementation is based on an octree and is designed to meet the following requirements:. It is possible to add new information or sensor readings at any time. Modeling and updating is done in a probabilistic. The extent of the map does not have to be known in advance. Instead, the map is dyna...

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