Speeding up rao-blackwellized particle filter SLAM with a multithreaded architecture

In this work we explore multiprocessor computer architectures to propose an effective method for solving the Simultaneous Localization and Mapping Problem. The proposed method makes use of multithreading to parallelize a Rao-Blackwellized Particle Filter approach. By applying the method in common computers found in robots, it is shown that a significant gain in efficiency can be obtained. Furthermore, the parallel method enables us to raise the number of particles up to values that would not be possible in a single threaded solution, thus gaining in localization precision and map accuracy. In order to analyze SLAM results, frequently used datasets by the robotics community were used, and a benchmarking metric was applied.