Abstract
We present a method to accelerate robot localization and mapping by using CUDA (Compute Unified Device Architecture), the general purpose parallel computing platform on NVIDIA GPUs. In robotics, the particle filter-based SLAM (Simultaneous Localization and Mapping) algorithm has many applications, but is computationally intensive. Prior work has used CUDA to accelerate various robot applications, but particle filter-based SLAM has not been implemented on CUDA yet. Because computations on the particles are independent of each other in this algorithm, CUDA acceleration should be highly effective. We have implemented the SLAM algorithm's most time consuming step, particle weight calculation, and optimized memory access by using texture memory to alleviate memory bottleneck and fully leverage the parallel processing power. Our experiments have shown the performance has increased by an order of magnitude or more. The results indicate that oftloading to GPU is a cost-effective way to improve SLAM algorithm performance.
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