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Autonomous machines (e.g., vehicles, mobile robots, drones) require sophisticated 3D mapping to perceive the dynamic environment. However, maintaining a real-time 3D map is expensive both in terms of compute and memory requirements, especially for resource-constrained edge machines. Probabilistic OctoMap is a reliable and memory-efficient 3D dense map model to represent the full environment, with dynamic voxel node pruning and expansion capacity. It is widely used but limited by its single-thread design. This paper presents the first efficient accelerator solution, i.e. OMU, to enable real-time probabilistic 3D mapping at the edge. The proposed 3D mapping accelerator is implemented and evaluated using a commercial 12 nm technology. Compared to the ARM Cortex-A57 CPU in the Nvidia Jetson TX2 platform, the proposed accelerator achieves up to 62 &#x00D7; performance and 708 &#x00D7; energy efficiency improvement. Furthermore, the accelerator provides 63 FPS throughput, more than 2 &#x00D7; higher than a real-time requirement, enabling real-time perception for 3D mapping.

OMU: A Probabilistic 3D Occupancy Mapping Accelerator for Real-time OctoMap at the Edge