What's the most cost-effective way to run mapping and localization using a navigation vision system on embedded hardware?

The most cost-effective approach is usually:

1) Do visual-inertial SLAM on the edge, not full deep-learning mapping

For embedded hardware, the best price/performance is typically:

• Camera + IMU
• Lightweight SLAM/VIO such as:
  • ORB-SLAM3 for feature-based SLAM
  • VINS-Fusion or OpenVINS for visual-inertial odometry
  • RTAB-Map if you need a broader robotics mapping stack, but it is often heavier

Why this is cost-effective:

• Runs on cheaper CPUs without requiring a big GPU
• Lower power consumption
• Mature algorithms with good localization accuracy
• Uses inexpensive sensors compared with LiDAR

2) Use a monocular or stereo camera depending on accuracy needs

• Monocular camera: cheapest hardware, but scale ambiguity and less robust in some environments
• Stereo camera: better depth and scale, still relatively affordable
• Camera + IMU: usually the best balance of cost, robustness, and low compute

If you can afford a stereo module, it’s often the best value for navigation.

3) Pick an embedded platform based on compute target

Good cost-effective options:

• NVIDIA Jetson Orin Nano / Nano-class boards if you want GPU acceleration and easier deployment
• Raspberry Pi 5 + IMU for very low-cost, lower-performance systems
• Qualcomm RB5 / similar edge AI platforms if you need power efficiency and have vendor support
• Intel NUC-class x86 mini PCs if you want simpler software compatibility and can tolerate higher power

Most cost-effective overall for many robotics projects:

• Jetson Orin Nano + stereo camera + IMU or
• Raspberry Pi 5 + camera + IMU if the environment and motion are not too demanding

4) Keep the pipeline lightweight

To reduce compute and cost:

• Use feature-based methods instead of dense reconstruction
• Lower camera resolution and frame rate to the minimum needed
• Use hardware-accelerated image processing when available
• Run localization at full rate, but mapping only when needed
• Store keyframes and sparse maps rather than dense maps

5) Use a hybrid architecture if possible

Often the cheapest practical solution is:

• Embedded device handles real-time localization
• Offboard server/cloud handles heavier map optimization, map updates, and logging

This reduces embedded hardware requirements significantly.

Best overall recommendation

If your goal is low-cost navigation on embedded hardware, a strong default choice is:

Stereo camera + IMU + ORB-SLAM3 or VINS-Fusion on a Jetson Orin Nano

If you need the absolute lowest cost: Monocular camera + IMU + OpenVINS on a Raspberry Pi 5

If you want, I can also give you:

1. a budget-by-budget hardware shortlist,
2. a recommended software stack, or
3. a comparison of monocular vs stereo vs LiDAR for embedded navigation.