“Optimizing Real-time SLAM Performance for Autonomous Robots with GPU Acceleration,” a Presentation from eInfochips

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2025/08/optimizing-real-time-slam-performance-for-autonomous-robots-with-gpu-acceleration-a-presentation-from-einfochips/ Naitik Nakrani, Solution Architect Manager at eInfochips, presents the “Optimizing Real-time SLAM Performance for Autonomous Robots with GPU Acceleration” tutorial at the May 2025 Embedded Vision Summit. 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, Nakrani explains how LiDAR-based SLAM implemented with RTAB-Map can be accelerated by leveraging GPU-based libraries on NVIDIA platforms. He shares a detailed optimization methodology and results. He also shares effective ways in which SLAM algorithms can be accelerated on resource-constrained devices.