OctoMap-RT: Fast Probabilistic Volumetric Mapping Using Ray-Tracing GPUs

A 3D occupancy map that is accurately modeled after real-world environments is essential for reliably performing robotic tasks. Probabilistic volumetric mapping (PVM) is a well-known environment mapping method using volumetric voxel grids that represent the probability of occupancy. The main bottleneck of current CPU-based PVM, such as OctoMap, is determining voxel grids with occupied and free states using ray-shooting. In this letter, we propose an octree-based PVM, called OctoMap-RT, using a hybrid of off-the-shelf ray-tracing GPUs and CPUs to substantially improve CPU-based PVM. OctoMap-RT employs massively paral- lel ray-shooting using GPUs to generate occupied and free voxel grids and to update their occupancy states in parallel, and it ex- ploits CPUs to restructure the PVM using the updated voxels. Our experiments using various large-scale real-world benchmarking environments with dense and high-resolution sensor measurements demonstrate that OctoMap-RT builds maps up to 41.2 times faster than OctoMap and 9.3 times faster than the recent SuperRay CPU implementation. Moreover, OctoMap-RT constructs a map with 0.52% higher accuracy, in terms of the number of occupancy grids, than both OctoMap and SuperRay.