Boxi: Design Decisions in the Context of Algorithmic Performance for Robotics

2025-04-25

AI Summary

Mobile robots exhibit limited robust autonomy in complex野外 environments due to a lack of empirical design knowledge for multimodal sensor systems. Method: This paper introduces Boxi—a tightly coupled, deeply optimized sensor payload specifically designed for state estimation and mapping. Boxi integrates dual LiDARs, ten heterogeneous RGB cameras (HDR/global/shutter-rolling), an RGB-D camera, a seven-unit heterogeneous IMU array, and dual-antenna RTK GNSS. It pioneers quantitative linkage between hardware design decisions—such as nanosecond-level hardware synchronization, cross-modal joint calibration, and heterogeneous IMU configuration—and VIO/SLAM performance. Contribution/Results: We open-source a reusable sensor “recipe” design guide. Real-world field experiments demonstrate that high-precision synchronization and multi-IMU redundancy reduce state estimation error by over 40%, significantly enhancing robustness in dynamic environment mapping and long-term autonomous navigation.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Multi-modal Vision
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

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

Problem

• Designing multimodal sensor suites for robust robot autonomy
• Optimizing sensor payloads for state estimation and mapping
• Addressing synchronization, calibration, and sensor modality challenges

Innovation

• Multimodal sensor suite for robust autonomy
• Time synchronization and calibration optimization
• Mobile sensor suite cookbook for design