Source: rock-master-19.06-slam-octomap
Section: science
Priority: extra
Maintainer: Rock Packaging Daemon <rock-dev@dfki.de>
Build-Depends: cdbs, debhelper (>= 8.0.0), pkg-config, dh-autoreconf, build-essential, cmake, libqglviewer-dev-qt4, libqt4-opengl-dev
Standards-Version: 3.9.2
Homepage: http://rock-robotics.org
#Vcs-Git: git://git.debian.org/collab-maint/bla.git
#Vcs-Browser: http://git.debian.org/?p=collab-maint/bla.git;a=summary

Package: rock-master-19.06-slam-octomap
Architecture: any
Depends: ${shlibs:Depends}, cmake, build-essential, libqt4-opengl-dev, libqglviewer-dev-qt4
Description: An Efficient Probabilistic 3D Mapping Framework Based on Octrees
 The OctoMap library 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:
  .
  Full 3D model. The map is able to model arbitrary environments without
  prior assumptions about it. The representation models occupied areas as
  well as free space. Unknown areas of the environment are implicitly
  encoded in the map. While the distinction between free and occupied space
  is essential for safe robot navigation, information about unknown areas is
  important, e.g., for autonomous exploration of an environment.
  .
  Updatable. It is possible to add new information or sensor readings at any
  time. Modeling and updating is done in a probabilistic fashion. This
  accounts for sensor noise or measurements which result from dynamic
  changes in the environment, e.g., because of dynamic objects. Furthermore,
  multiple robots are able to contribute to the same map and a previously
  recorded map is extendable when new areas are explored.
  .
  Flexible. The extent of the map does not have to be known in advance.
  Instead, the map is dynamically expanded as needed. The map is
  multi-resolution so that, for instance, a high-level planner is able to
  use a coarse map, while a local planner may operate using a fine
  resolution. This also allows for efficient visualizations which scale from
  coarse overviews to detailed close-up views.
  .
  Compact. The map is stored efficiently, both in memory and on disk. It is
  possible to generate compressed files for later usage or convenient
  exchange between robots even under bandwidth constraints.

