الفهرس 
Fusing 3D maps built by a team of robots
Background and Related WorkOnly 14 pages are availabe for public view
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Abstract
The numerous benefits in many applications, especially in search and rescue missions idea of making a team of mobile robots perform a collaborative task has
and fire fighting.
In this operation scenario a dense three dimensional map of the operation environment will be of great use, but usually it is either not present or it has been changed
because of the fire or the collapses in the building.
This research addresses the problem of three dimension map construction using a
team of cooperative mobile robots each equipped with a visual sensor. A framework
for a collaborative map construction system is proposed along with a comprehensive
overview of the state-of-art visual simultaneous localization and mapping algorithms.
Depending only on the vision sensor to perform the complex task of localization
and mapping introduces many challenges in the proposed algorithm because usually
the data provided by the sensors are noisy and sometimes they interfere with the map
updating process resulting in false matches. The use of a team of robots also introduces
many challenges such as maintaining a coherent team behaviour, communication between team members, complex tasks decomposition, subtasks assignment and aligning
and merging the partial maps constructed by individual robots into one coherent map
which is the focus of this study.
The research problem was divided into two parts: first building three dimensional
maps using the observations of a single mobile robot applied many times on different
parts of the environment, second aligning and merging the m maps constructed by
individual robots with different views into one global consistent map.
The map building algorithm was divided into three main parts: registration, loop
closure and global optimization. In registration two successive observations of a single robot were aligned together. This resulted in an erroneous map due to the accumulation
of small errors in the registration algorithm and errors in the sensed data causing the
resulting map to drift over time. These errors are detected when the robot visits a
location that has already been mapped before. The loop is closed and the drift is
calculated. Finally global optimization is applied and the drift is corrected along the
whole path covered so far.
The aligning and merging algorithm takes the m maps produced from the mapping
algorithm and work in pairs aligning and merging them together using the robots
starting positions as an initial guess.
The proposed system was evaluated on standard datasets of indoor environments.
The evaluation showed that the absolute relative pose error between the estimated
robot poses could be reduced to 0.01 meters and 0.56 degrees. Furthermore the results
were compared with other state-of-art algorithms showing the strengths and weaknesses
of the proposed system.