Recent robotics applications require 3-D representations of the environments. In many cases, it is not feasible for a single robot to map the entire environment. In these cases, it is necessary for a team of robots to build maps independently and merge them into a single global map. In this paper, octree-based occupancy grids, which are currently the state-of-the-art 3-D map representation, are applied to the problem of multirobot mapping. Octrees allow large environments to be mapped efficiently, in terms of memory usage, while still providing sufficiently fine resolution where required. The main contribution of this work lies in the definition and validation of a system, which use map data from commonly mapped portions of the environment with registration techniques, such that maps are merged coherently despite measurement noise and error in the relative transformations between maps for experimental data sets. The system defined can then be used in a complete solution that is ported to mobile robots. The results demonstrate that octree occupancy grids are a suitable representation for multirobot 3-D mapping, but that the proposed techniques for improving erroneous transformation estimates between map frames allow multiple maps to be merged efficiently and robustly.

A technique for merging 3D octree based occupancy grid maps is proposed and implemented. Octrees are a memory efficient way to represent a 3D environment by recursively subdividing space at multiple depths in a tree structure. The use of of an octree representation of a 3D environment allows large environments to be mapped while limiting the amount of memory used in comparison to other techniques. When multiple robots are used to map an environment a more accurate map of a larger space can be produced in less time. In this paper, the problem of merging octree based occupancy grid maps from independent robots into one global map of their environment is explored.Techniques are introduced to address information from sources coming from multiple depths in the map as well as relative transformations between maps that are not axis aligned. These techniques allow the octree representation of an environment to be extended to multiple robots. The application of these techniques is demonstrated by merging maps built by robots in a simulated environment. The contribution of this work lies in the introduction of a feasible method of merging memory efficient maps of a 3D environment. The results obtained in this paper demonstrate that the proposed strategies for octree based map mergers are valid.

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