Hardware Kit

x86_64 + CUDA

Desktop/workstation class CPU paired with a discrete NVIDIA GPU. Best for large maps, high-rate scan matching, and accelerated perception.

CUDA Workstation High throughput

Compute

Multi-core x86_64 + discrete GPU

Memory

16-256 GB RAM + GPU VRAM

OS/RTOS

Linux / Windows

Toolchain

nvcc, CUDA Toolkit, Nsight

Power

200W+ system-dependent

Typical Boards

Desktop workstations with NVIDIA RTX/A-series GPUs
Industrial GPU PCs for deployment
Rack servers for lab-scale runs

Toolchain + Build Profile

CUDA Toolkit + nvcc
Set CMAKE_CUDA_ARCHITECTURES to your GPU compute capability
CMake with -DENABLE_CUDA=ON
Nsight for profiling

cmake .. -DTARGET=cuda -DENABLE_CUDA=ON
cmake --build .

Pin-Level + Electrical

I/O via PCIe/USB devices (CAN, GPIO, serial)
Use dedicated interface boards for deterministic I/O
Ensure GPU driver/toolkit versions match

Sensors + Peripherals

High-bandwidth sensors over Ethernet/USB/PCIe
Multi-camera pipelines with GPU acceleration

Comms + Networking

10GbE/5G as needed for large data flows
CAN/serial via PCIe adapters

Real-Time + Determinism

Use CPU core isolation for deterministic loops
Batch GPU work to avoid launch overhead

Memory + Thermal Constraints

Large maps can exceed VRAM if resolution and volume are not bounded.
Continuous high-rate kernels require thermal headroom and stable airflow.
Monitor GPU clocks and memory pressure during representative worst-case runs.

Recommended Vault Modules

3D Voxel Occupancy + CUDA NDT-lite Scan Matching (CUDA) Costmap Inflation (CUDA) Occupancy Grid Mapping (CUDA) Scan Matching (CUDA) Frontier Exploration Flight Data Recorder Path Following

Pitfalls + Mitigations

Driver/toolkit mismatch causes runtime failures
GPU memory over-commit with large maps
Power/thermal design for sustained GPU load

Field Checklist

Validate CUDA toolkit and driver version
Monitor VRAM usage under worst-case maps
Pin CPU threads and batch GPU kernels