Optimizing execution time of long-term and large-scale SLAM algorithms is essential for real-time deployments on edge compute platforms. Faster SLAM output means faster map refresh rates and quicker decision-making. RTAB-Map is a popular state-of-the-art SLAM algorithm used in autonomous mobile robots. RTAB-Map is implemented in an open-source library that supports various sensors, including RGB-D cameras, stereo cameras and LiDAR. In this talk, we will explain how LiDAR-based SLAM implemented with RTAB-Map can be accelerated by leveraging GPU-based libraries on NVIDIA platforms. We will share a detailed optimization methodology and results. We will also share effective ways in which SLAM algorithms can be accelerated on resource-constrained devices.