Parallel and Cached Scan Matching for Robotic 3D Mapping

Intelligent autonomous acting of mobile robots in unstructured environments requires 3D maps. Since manual mapping is a tedious job, automatization of this job is necessary. Automatic, consistent volumetric modeling of environments requires a solution to the simultaneous localization and map building problem (SLAM problem). In 3D task is computationally expensive, since the environments are sampled with many data points with state of the art sensing technology. In addition, the solution space grows exponentially with the additional degrees of freedom needed to represent the robot pose. Mapping environments in 3D must regard six degrees of freedom to characterize the robot pose. This paper summarizes our 6D SLAM algorithm and presents novel algorithmic and technical means to reduce computation time, i.e., the data structure cached \kd~tree and parallelization. The availability of multi-core processors as well as efficient programming schemes as OpenMP permit the parallel execution of robotics tasks