FRAGG-Map: Frustum Accelerated GPU-Based Grid Map

In robotics, occupancy grids serve as required repositories of information about the environment in numerous applications. One such critical application is Simultaneous Localization and Mapping (SLAM), where robots dynamically scan and explore their surroundings while in motion. In the context of extended-duration missions, it becomes imperative to confront the complexities linked to the expansion of occupancy grids as well as handling loop closure detection. These challenges primarily revolve around two key aspects: enabling the seamless expansion of the map on multiple occasions, thus avoiding the need to map smaller regions in numerous separate missions, and ensuring real-time updates to the map to sustain the robot’s knowledge base and enhance its responsiveness. To address these challenges, we introduce an innovative map called Frustum Accelerated GPU-Based Grid Map (FRAGG-Map). This map adopts a highly parallelizable 3D grid structure and leverages the power of CUDA kernels to facilitate efficient insertion of point-clouds and enables real-time updates of the map. FRAGG- Map identifies the portions of the map that require updates and utilises the GPU to update them, significantly enhancing computational performance. Our results show that FRAGG- Map can run 31 times faster than OctoMap, significantly outperforming state-of-the-art methods.