智能机器人同步定位与建图专用芯片研究综述

刘炳强, 沈梓煊, 王继鹏, 肖健, 谭玉龙, 何再生, 许登科, 王珂, 瞿卫新, 王超, 孙立宁

集成电路与嵌入式系统 ›› 2024, Vol. 24 ›› Issue (11) : 1-14.

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集成电路与嵌入式系统 ›› 2024, Vol. 24 ›› Issue (11) : 1-14. DOI: 10.20193/j.ices2097-4191.2024.0027
智能机器人高能效专用芯片研究专栏

智能机器人同步定位与建图专用芯片研究综述

作者信息 +

Overview of development and challenges of dedicated chips for simultaneous localization and mapping in intelligent robotics

Author information +
文章历史 +

摘要

机器人是新质生产力的革命性引擎,正在重塑人类的生活和工作。同步定位与建图技术(Simultaneous Localization And Mapping,SLAM)能够使机器人在未知环境中自主导航并构建周围环境的地图,是自主移动机器人实现智能化的基石。然而,SLAM算法复杂且运算量大,基于通用芯片方案实现存在延时长、功耗高的问题,不能满足自主移动机器人,尤其是小型、微型、纳型机器人的实时性、体积和功耗需求。因此,设计专用芯片加速计算密集的SLAM算法在近年来受到学术界和产业界的高度关注。本文首先从SLAM技术的基本概念和应用场景出发介绍了SLAM算法需要硬件加速的必要性,接着从算法和专用芯片设计两个角度出发梳理了SLAM技术的研究现状与发展趋势,接着重点讨论了SLAM专用芯片研究的技术挑战与解决方案,对未来发展给出了建议。

Abstract

Robots represent a revolutionary engine of new productive forces, reshaping human life and work. Simultaneous Localization And Mapping (SLAM) technology enables robots to navigate autonomously in unknown environments and construct maps of their surroundings, serving as the cornerstone for the intelligence of autonomous mobile robots. However, given that SLAM algorithms are complex and computationally intensive, implementations based on general-purpose CPU chips suffer from long delays and high power consumption, which fails to meet the real-time and power consumption requirements of autonomous mobile robots, especially small, micro, and nano ones. Consequently, the design of specialized hardware accelerator chips to accelerate computation-intensive SLAM algorithms has received considerable attention from both the academic and industrial communities in recent years. This article starts with the basic concepts and application scenarios of SLAM technology, and highlights the necessity of hardware acceleration for SLAM algorithms. It then reviews the current research status and development trends from the perspectives of algorithms and dedicated chip design, and discusses the technical challenges and solutions related to SLAM dedicated chips, providing recommendions for future development.

关键词

机器人 / 同步定位与建图 / 专用芯片 / 硬件加速 / SLAM

Key words

robots / simultaneous localization and mapping / specialized chips / hardware acceleration / SLAM

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刘炳强, 沈梓煊, 王继鹏, 等. 智能机器人同步定位与建图专用芯片研究综述[J]. 集成电路与嵌入式系统. 2024, 24(11): 1-14 https://doi.org/10.20193/j.ices2097-4191.2024.0027
LIU Bingqiang, SHEN Zixuan, WANG Jipeng, et al. Overview of development and challenges of dedicated chips for simultaneous localization and mapping in intelligent robotics[J]. Integrated Circuits and Embedded Systems. 2024, 24(11): 1-14 https://doi.org/10.20193/j.ices2097-4191.2024.0027
中图分类号： TN47 (大规模集成电路、超大规模集成电路)   

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https://doi.org/10.15302/J-SSCAE-2022.04.007
摘要
智能机器人正在引领全球新一轮的科技革命和产业变革，培育并推进我国智能机器人核心芯片技术及产业发展，有助 于产业优化升级并实现生产力跃升。本文阐述了智能机器人核心芯片技术对于推动技术自主可控、实现经济高质量发展、满 足居民美好生活需要、提升国家核心竞争力等方面的重要价值；梳理了相关政策、技术、产业等的国际进展，分析了我国发 展智能机器人核心芯片的基础优势和面临的问题；以多架构路线、技术方案比对的方式，论证了我国智能机器人芯片技术发 展路线，据此提出领域发展策略，形成面向2035的重点任务与发展路线图。研究建议，将智能机器人芯片自主可控发展上 升为国家战略，明确顶层设计；设立智能机器人芯片重大科技专项，加大科研投入；出台激励智能机器人芯片技术研究和产 业应用的政策，牵引产业链升级；落实智能机器人芯片人才培养和发展措施，推动技术及产业健康发展。
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基金

国家重点研发计划机器人环境建模与导航定位专用芯片及软硬件模组(2019YFB1310000)
武汉市科技重大专项“卡脖子”技术攻关项目(2022010402020045)
华中科技大学交叉研究支持计划(2024JCYJ036)
华中科技大学未来技术太湖创新基金(2023-B-6)

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