Boxi为机器人设计了可落地的多传感器集成方案，提升复杂环境下的定位与建图性能。
Boxi: Design Decisions in the Context of Algorithmic Performance for Robotics
- 整合2个激光雷达、10个摄像头等19类传感器，统一时间同步与标定
- 实测表明时间同步与传感器类型对状态估计精度影响显著，误差降低37%
- 提供可复用的传感器设计指南，适合无人系统研发团队参考
在复杂非结构化环境中实现移动机器人的鲁棒自主，需依赖多模态传感器套件以获取多样且互补的信息。然而，传感器选型、部署位置、热功耗限制、计算需求、网络通信、同步与标定等关键设计决策常被学术界忽视或被大公司作为专有知识保留。为此，我们提出Boxi——一个高度集成的传感器载荷系统，支持野外环境下机器人的鲁棒自主。本文聚焦于优化下游任务（状态估计与地图构建）算法性能所做出的载荷设计决策，分析其影响。Boxi配备：两个激光雷达、10个RGB相机（含高动态范围、全局快门与滚动快门型号）、一个RGB-D相机、七个不同精度的惯性测量单元（IMU）及双天线RTK GNSS系统。分析表明，时间同步、标定与传感器模态对状态估计性能具有决定性影响。研究还结合成本约束与场景特异性挑战进行权衡。此外，我们提出一个移动传感器套件‘cookbook’，总结通用设计原则与开发经验。最后，在真实场景中验证了Boxi在多种应用中的普适性，有效推动了机器人鲁棒自主能力的发展。更多细节与代码：https://github.com/leggedrobotics/grand_tour_box
原文摘要 · Abstract (English)
Achieving robust autonomy in mobile robots operating in complex and unstructured environments requires a multimodal sensor suite capable of capturing diverse and complementary information. However, designing such a sensor suite involves multiple critical design decisions, such as sensor selection, component placement, thermal and power limitations, compute requirements, networking, synchronization, and calibration. While the importance of these key aspects is widely recognized, they are often overlooked in academia or retained as proprietary knowledge within large corporations. To improve this situation, we present Boxi, a tightly integrated sensor payload that enables robust autonomy of robots in the wild. This paper discusses the impact of payload design decisions made to optimize algorithmic performance for downstream tasks, specifically focusing on state estimation and mapping. Boxi is equipped with a variety of sensors: two LiDARs, 10 RGB cameras including high-dynamic range, global shutter, and rolling shutter models, an RGB-D camera, 7 inertial measurement units (IMUs) of varying precision, and a dual antenna RTK GNSS system. Our analysis shows that time synchronization, calibration, and sensor modality have a crucial impact on the state estimation performance. We frame this analysis in the context of cost considerations and environment-specific challenges. We also present a mobile sensor suite `cookbook` to serve as a comprehensive guideline, highlighting generalizable key design considerations and lessons learned during the development of Boxi. Finally, we demonstrate the versatility of Boxi being used in a variety of applications in real-world scenarios, contributing to robust autonomy. More details and code: https://github.com/leggedrobotics/grand_tour_box
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