Robust and Efficient Volumetric Occupancy Mapping

The provided source code package contains an efficient and robust method for volumetric occupancy mapping. This method was specifically designed to provide robust results when used in conjunction with noisy measurements, as they result from stereo matching. This method is an extension of OctoMap, which you will require for compiling the provided source code. Details about our method can be found in [1].


Build Instructions

For compiling the provided source code you require an installation of OctoMap 1.6.x (tested for 1.6.0 - 1.6.3), which can be obtained from:

http://octomap.github.io

The code can be compiled using the typical CMake build process:

> cmake .
> make

How to Use

After compilation you will find a library in ./lib. All necessary header files are located in ./inc.

The main class of this library is occmapping::RobustOcTree. Using this class is equivalent to octomap::OcTree, for which you can find the documentation at:

http://octomap.github.io/octomap/doc/

The main differences are:
  • Relevant parameters are set by passing an instance of occmapping::RobustOcTreeParameters to the constructor of occmapping::RobustOcTree. The default constructor of this parameter class already sets a reasonable set of default parameters.
  • Ray casting always assumes a maximum range. This parameter is no longer passed as an argument to all ray casting methods, but is set in the parameter class.
  • Change notifications and bound box limits are currently not supported.

Further Examples

OctoMap Proposed Method

Notes

Parts of the provided code are based on the original OctoMap implementation by K.M. Wurm and A. Hornung. The code is licensed under the new BSD license, of which a copy is distributed with the code.

Downloads

occmapping.tar.bz2 (12 KB)

Contact

Konstantin Schauwecker, konstantin.schauwecker-NO_SPAM-@uni-tuebingen.de

References

[1] Konstantin Schauwecker and Andreas Zell. Robust and Efficient Volumetric Occupancy Mapping with an Application to Stereo Vision. In IEEE International Conference on Robotics and Automation (ICRA), pages 6102-6107, Hong Kong, China, May 2014. [ pdf ]