Things used in this project
Hardware components
| MentorPi M1 Chassis |
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| Bracket Set |
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| Raspberry Pi 5 |
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| 64 GB SD Card |
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| Cooling Fan |
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| Raspberry Pi Power Supply C |
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| RPC Lite Controller + RPC Data Cable |
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| Battery Cable |
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| Lidar + 4PIN Wire |
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| Lidar Data Cable |
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| 8.4V 2A Charger (DC5.5*2.5 Male Connector) |
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| 3D Depth Camera |
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| Depth Data Cable |
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| Wireless Controller |
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| Controller Receiver |
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| EVA Ball (40mm) |
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| Card Reader |
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| 3PIN Wire (100mm) |
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| WonderEcho Pro AI Voice Interaction Box + Type C Cable |
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| Accessory Bag |
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| User Manual |
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Story
Like many developers, I once had that cool vision: using the powerful Raspberry Pi 5 as a brain, coupled with a camera, to build a robot that could see and understand the world in real time. However, when you actually start trying to build an AI vision development environment from scratch on a Pi 5, that beautiful vision is often quickly replaced by a cold, harsh reality.
You don’t encounter a single technical problem; you face a towering wall built from countless fragmented challenges. From camera drivers and compiling OpenCV with its myriad dependencies, to converting and accelerating AI models for the ARM architecture, and finally integrating recognition results with a robot control system... Each step can consume days, often ending in the frustratingly low frame rates (typically below 1 FPS) that make projects feel useless. Throughout this process, almost 95% of your energy is spent just "getting the environment to run, " not on what you actually wanted to do: "bringing your ideas to life."
Today, I want to share a completely different experience. Using the Hiwonder MentorPi M1 — an all-in-one robotics platform — I compressed that weeks- or even months-long exploration process into an incredible 10 minutes. Let’s break down the core struggles and the solution that bypasses them entirely.
Stuck in the Mud: The "Hidden" Cost of Native AI Vision on Pi 5
First, we must acknowledge why achieving usable native AI vision on a Raspberry Pi 5 is so notoriously difficult. Its core computing power is indeed strong, but as a general-purpose single-board computer, it doesn’t provide a ready-made path for the complex task of "robotic vision."