When I started the Soohoo Labs platform, the two obvious choices for stereo depth were the Intel RealSense D435 and the Luxonis OAK-D Lite. I bought both. Here's what I found.
The Short Version
OAK-D Lite wins on three things that mattered for our build: on-device AI inference (4 TOPS MyriadX), ROS2 integration quality, and power consumption. RealSense wins on one thing: better depth accuracy at 0.3–0.8m range. For a navigation robot doing obstacle avoidance from 0.5m+, OAK-D's range is fine.
| SPEC | OAK-D LITE | REALSENSE D435 |
|---|---|---|
| Price | $149 | $179 |
| Depth range | 0.3–8m | 0.2–10m |
| On-device AI | 4 TOPS MyriadX | None |
| RGB resolution | 4K @ 30fps | 1080p @ 30fps |
| ROS2 support | depthai_ros (active) | realsense-ros (lagging) |
| USB | USB-C 3.1 | USB-C 3.1 |
| Power (idle) | ~2W | ~3.5W |
| Power (full load) | ~2.5W | ~4.8W |
Why On-Device AI Changed the Architecture
The MyriadX in OAK-D Lite can run person detection, object classification, or pose estimation without touching the Jetson's compute budget. On our reference platform, we run a MobileNetV2-based person detector on the OAK-D at 25fps, feeding bounding boxes over USB. The Jetson handles navigation and higher-level reasoning. This offload matters: it's the difference between a robot that can do spatial AI *and* navigate simultaneously, or one that has to choose.
You can load custom ONNX models onto OAK-D Lite via the DepthAI API. Conversion is 10-30 minutes on a desktop with 16GB RAM. Once compiled to .blob, it runs on-device without cloud dependency.
Where RealSense D435 Actually Wins
Close-range accuracy. RealSense D435 with its active IR projector maintains accurate depth down to 0.2m with much less noise than OAK-D Lite's passive stereo at that range. For a robot arm or a manipulation build where you're working at 15-40cm, D435 is the right choice. For a mobile robot navigating at 0.5m+, OAK-D Lite's passive stereo is fine.
We also tested the RealSense D455, which has a better baseline (95mm vs 50mm on D435) and range up to 6m. It's the right choice for outdoor or large-space navigation — but at $299 it's out of scope for our $400-500 platform target.
The ROS2 Integration Reality Check
As of JetPack 6.0 + ROS2 Humble (June 2026), the depthai_ros package installs and compiles cleanly in under 15 minutes. The realsense-ros package has known issues with the librealsense2 library on Jetson ARM64 — you'll likely spend 1-3 hours patching cmake files and resolving CUDA version conflicts. That time cost is real and repeatable.
librealsense2 has known ARM64 build issues with CUDA 12.x. Check the Intel RealSense GitHub issues before committing to the platform on Jetson.
Bottom Line
OAK-D Lite is in both Room-Nav and Mobile-Light kits because it's the right tool for autonomous indoor navigation with AI at edge. If you're building a manipulation arm or need sub-0.3m depth accuracy, substitute the D435 — the BOM swap is straightforward, just budget extra time for the ROS2 integration.