GridSLAM is an easy to use and understand Rao-Blackwellized particle filer to learn grid maps from laser range data.
Further information

Dirk Haehnel; Dieter Fox; Wolfram Burgard; Sebastian Thrun;

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Long Description
The ability to learn a consistent model of its environment is a prerequisite for autonomous mobile robots. A particularly challenging problem in acquiring environment maps is that of closing loops; loops in the environment create challenging data association problems. This work presents a novel algorithm that combines Rao-Blackwellized particle filtering and scan matching. In our approach scan matching is used for minimizing odometric errors during mapping. A probabilistic model of the residual errors of scan matching process is then used for the resampling steps. This way the number of samples required is seriously reduced. Simultaneously we reduce the particle depletion problem that typically prevents the robot from closing large loops. We present extensive experiments that illustrate the superior performance of our approach compared to previous approaches.

Input Data
The approach takes raw laser range data and (scan-matched) odometry.

Logfile Format
Carmen log format as well as Dirk Haehnel's rec format

Type of Map
grid maps

Hardware/Software Requirements
Linux/Unix, GCC 3.3/4.x

Papers Describing the Approach
D. Haehnel, D. Fox, W. Burgard, S. Thrun: A highly efficient FastSLAM algorithm for generating cyclic maps of large-scale environments from raw laser range measurements, In Proc. of the Conference on Intelligent Robots and Systems (IROS), 2003 (link)

License Information
This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
The authors allow the users of to use and modify the source code for their own research. Any commercial application, redistribution, etc has to be arranged between users and authors individually and is not covered by

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