Consumer-grade drones have become effective multimedia collection tools, spring-boarded by rapid development in embedded CPUs, GPUs, and cameras. They are best known for their ability to cheaply collect high-quality aerial video, 3D terrain scans, infrared imagery, etc., with respect to manned aircraft. However, users can also create and attach custom sensors, actuators, or computers, so the drone can collect different data, generate composite data, or interact intelligently with its environment, e.g., autonomously changing behavior to land in a safe way, or choosing further data collection sites. Unfortunately, developing custom payloads is prohibitively difficult for many researchers outside of engineering. We provide guidelines for how to create a sophisticated computational payload that integrates a Raspberry Pi 5 into a DJI Matrice 350. The payload fits into the Matrice's case like a typical DJI payload (but is much cheaper), is easy to build and expand (3D-printed), uses the drone's power and telemetry, can control the drone and its other payloads, can access the drone's sensors and camera feeds, and can process video and stream it to the operator via the controller in real time. We describe the difficulties and proprietary quirks we encountered, how we worked through them, and provide setup scripts and a known-working configuration for others to use.

翻译：消费级无人机已凭借嵌入式CPU、GPU和摄像头技术的快速发展，成为高效的多媒体采集工具。相较于有人驾驶飞行器，其最突出的优势在于能以低成本获取高质量航拍视频、三维地形扫描图、红外影像等数据。然而，用户同样可以自主创建并挂载定制传感器、执行器或计算机系统，使无人机能够采集异类数据、生成复合数据，或与环境进行智能交互（例如自主改变行为实现安全着陆，或选择后续数据采集点）。遗憾的是，对于工程领域外的众多研究者而言，开发定制任务载荷的难度令人望而却步。本文提供了将Raspberry Pi 5集成至DJI Matrice 350中的复杂计算任务载荷创建指南。该任务载荷可像标准DJI载荷一样嵌入Matrice机身（但成本显著降低），具备易于构建和扩展（3D打印）的特性，能够利用无人机电源与遥测系统，可控制无人机及其挂载的其他载荷，能访问无人机传感器与摄像头数据流，并支持实时处理视频画面通过遥控器传输至操作端。我们详细说明了开发中遇到的专有技术难点与特殊性问题、解决方案，并提供了可直接使用的配置脚本与已验证的系统配置。