2024 年至 2031 年按地图类型、产品、应用、最终用户和地区划分的同步定位和地图绘製 (SLAM) 市场

2024 年至 2031 年同步定位与地图绘製 (SLAM) 市场评估

同步定位和地图绘製是一种使设备或机器人能够即时瞭解和绘製其环境，同时确定其自身在该环境中的位置的技术。这使得军事和国防、製造业和许多其他领域的应用非常有效率。据 Verified Market Research 分析师称，2023 年全球同步定位和地图绘製市值将达到 2.62 亿美元。预计到 2031 年收入将达到 18 亿美元。

市场扩张归因于多种因素，包括对 AR/VR 应用的需求不断增加、自动驾驶汽车的普及以及感测器技术的进步。 SLAM 应用的激增将推动市场在 2024 年至 2031 年期间以 41.6% 的复合年增长率成长。

同步定位和地图绘製 (SLAM) 市场：定义/概述

定位与绘图同时进行是藉助无人驾驶车辆或机器人在环境中导航来创建地图的过程。 SLAM 是一种用于机器人製图或机器人测绘的系统。该过程涉及使用复杂的计算、演算法和感官输入进行导航。这使得可以远端建立地理资讯系统 (GIS) 数据，即使在人类无法绘製地图的危险环境中也是如此。开发或升级地图时遇到的计算困难称为同时定位和地图绘製。

专为SLAM应用而设计的机器人称为SLAM机器人。 SLAM（同步定位和地图建构）是机器人和无人驾驶汽车采用的技术，用于同时生成地图并使用它来导航其环境。由于视觉 SLAM 系统需要即时运行，因此它们会定期分别对位置和映射资料进行捆绑调整，但同时进行以加快最终的整合。 SLAM 技术有许多应用，包括扩增实境、虚拟影像投影和广泛的现场机器人技术。同步定位和映射技术显着提高了准确性。

哪些因素推动了全球 SLAM（同步定位和地图绘製）市场的发展？

全球 SLAM 市场受到推动其采用和成长的几个关键因素的驱动。一个关键因素是各行各业对自主移动机器人和车辆的需求不断增长。这些机器人和车辆依靠 SLAM 技术来精确导航和绘製周围环境，无需人工干预。

随着製造业、物流业和农业等行业自动化程度的提高，对强大的 SLAM 解决方案的需求也日益增加。扩增实境（AR）和虚拟实境（VR）应用越来越受欢迎。 SLAM 技术透过即时精确追踪使用者的位置和周围环境，在实现沉浸式 AR 体验方面发挥关键作用。

在虚拟实境应用中，SLAM 透过映射实体空间和无缝整合数位内容来促进可信任的虚拟环境的创建。游戏、娱乐、教育和企业应用中 AR 和 VR 的使用情况日益增多，推动了对先进 SLAM 解决方案的需求。

此外，感测器技术的进步，特别是光达、摄影系统和惯性感测器领域的进步，显着提高了 SLAM 演算法的准确性和可靠性。这些技术进步正在推动能够在各种环境和课题中运行的更强大和高效的 SLAM 系统的发展。因此，机器人、汽车和家电等各个行业都面临着越来越大的课题，需要将 SLAM 技术融入他们的产品和服务中，以提高其性能和功能。

哪些问题导致SLAM销售暴跌？

儘管机会光明，但全球 SLAM 市场仍面临着一些可能阻碍其采用和成长的课题。 SLAM 演算法的复杂性和计算严谨性，尤其是对于即时应用。开发一个能够准确绘製环境地图并即时追踪位置同时有效管理运算资源的强大 SLAM 系统仍然是一个技术障碍。

另一个课题是在多样化和动态环境中实现高精度和可靠性，例如户外和杂乱的室内空间。 SLAM 系统与现有硬体和软体平台的整合和互通性。包括机器人、汽车和扩增实境在内的许多行业都依赖各种各样的硬体组件和软体框架。确保 SLAM 解决方案与这些现有平台的无缝整合和相容性可能很困难，并且需要大量的客製化和开发工作。此外，不同 SLAM 系统和标准之间的互通性问题可能会对协作造成障碍，并阻碍基于 SLAM 的应用程式在不同产业中的可扩展性。

与 SLAM 技术相关的隐私和安全问题带来了课题，尤其是在涉及敏感资料或环境的应用中。由于 SLAM 系统依靠摄影机和光达等感测器来收集和处理有关物理空间的数据，因此人们担心潜在的隐私侵犯和未经授权存取敏感资讯。解决这些问题并采用强大的安全措施来保护资料的隐私和完整性对于培养对 SLAM 技术的信任和采用至关重要。

目录

第 1 章 全球 SLAM（同步定位与地图建构）市场：简介

市场概况
• 研究范围
• 先决条件

第 2 章执行摘要

第 3 章：经过验证的市场研究方法

• 资料探勘
• 验证
• 主要来源
• 资料来源列表

第四章 全球 SLAM（同步定位与地图建构）市场展望

• 概述
• 市场动态
  • 驱动程式
  • 阻碍因素
  • 机会
• 波特五力模型
• 价值链分析

第 5 章 全球同步定位与地图绘製 (SLAM) 市场（按产品）

• 概述
• 稀疏方法与密集方法
• 直接法与间接法

第六章 全球同步定位和地图绘製 (SLAM) 市场（按应用）

• 概述
• 移动机器人
• 智慧扩增实境
• 其他

第 7 章。

• 概述
• 北美洲
  • 美国
  • 加拿大
  • 墨西哥
• 欧洲
  • 德国
  • 英国
  • 法国
  • 其他欧洲国家
  亚太地区
  • 中国
  • 日本
  • 印度
  • 其他亚太地区
• 世界其他地区
  • 拉丁美洲
  • 中东和非洲

第 8 章。

• 概述
• 各公司的市场排名
• 主要发展策略

第九章 公司简介

• Google
• Microsoft
• Uber
• Sony
• Clearpath Robotics
• Vecna
• Locus Robotics
• Fetch Robotics
• IRobot
• LG Electronics

第 10 章 重大进展

• 产品发布/开发
• 合併和收购
• 业务扩展
• 伙伴关係和合作关係

第 11 章附录

• 相关研究

Simultaneous Localization and Mapping (SLAM) Market Valuation - 2024-2031

Simultaneous Localization and Mapping is a technology that enables devices or robots to understand and map their environment in real-time while simultaneously determining their own position within that environment. Thereby, rendering highly efficient for further application in the military and defense, manufacturing, and other diverse sectors. According to the analyst from Verified Market Research, the Global Simultaneous Localization and Mapping Market has valuation of USD 262 Million in 2023. The forecast by subjugating the revenue of USD 1.8 Billion in 2031.

The market proliferation predominantly ascribes to numerous factors, such as the rising demand for AR/VR applications, the increasing adoption of autonomous vehicles, and advancements in sensor technologies. This upsurge in the application of SLAM enables the market to grow at aCAGR of 41.6% from 2024 to 2031.

Simultaneous Localization and Mapping (SLAM) Market: Definition/ Overview

Simultaneous localization and mapping is the process of creating a map with the help of an unmanned vehicle or a robot that navigates the environment. Simultaneous localization and mapping is a system used in robot cartography or robot mapping. This procedure employs a complex array of computations, algorithms, and sensory inputs to navigate. It allows for the remote creation of geographic information system (GIS) data in situations where the surroundings are dangerous for humans to map. A computational difficulty encountered during map development or upgrade is referred to as simultaneous localization and mapping.

Robots that have been designed to serve the purpose of SLAM applications are referred to as SLAM robots. Simultaneous localization and mapping (SLAM) is a technique employed by robots or unmanned vehicles to generate a map while simultaneously navigating the environment, utilizing the map it generates. Visual SLAM systems need to operate in real-time, so regularly location and mapping data suffer bundle adjustment separately, but simultaneously to facilitate faster processing speeds before they're ultimately merged. The SLAM technology has numerous applications, including augmented reality, projecting virtual images, and a diverse range of field robots. The accuracy has greatly improved with the help of simultaneous localization and mapping technology.

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Which are the Drivers Encouraging the Global Simultaneous Localization and Mapping (SLAM) Market?

The Global SLAM market is being driven by several key factors that are driving its adoption and growth. One significant factor is the escalating demand for autonomous mobile robots and vehicles across diverse industries. These robotics and vehicles rely on SLAM technology to navigate and map their surroundings accurately without human intervention.

As industries such as manufacturing, logistics, and agriculture continue to automate their operations, the demand for robust SLAM solutions continues to grow. The escalating popularity of augmented reality (AR) and virtual reality (VR) applications. SLAM technology has a crucial role in enabling immersive AR experiences by accurately tracking the user's position and surroundings in real time.

In virtual reality applications, SLAM facilitates the creation of authentic virtual environments by mapping physical spaces and seamlessly integrating digital content. The increasing use cases for AR and VR in gaming, entertainment, education, and enterprise applications are driving demand for advanced SLAM solutions.

Furthermore, advances in sensor technology, particularly in the fields of LIDAR, camera systems, and inertial sensors, have greatly improved the accuracy and reliability of SLAM algorithms. These technological advances have led to the development of more robust and efficient SLAM systems that are capable of operating in diverse environments and under challenging conditions. Consequently, various industries, such as robotics, automotive, and consumer electronics, challenges are increasingly incorporating SLAM technology into their products and services to enhance their performance and functionality.

What are the Challenges Plummeting the Sales of Simultaneous Localization and Mapping?

Despite the promising opportunities, the global SLAM market faces several challenges that could hinder its widespread adoption and growth. The complexity and computational rigor of SLAM algorithms, particularly in the context of real-time applications. The development of robust SLAM systems that are capable of precisely mapping environments and tracking positions in real time while efficiently managing computational resources, remains a technical obstacle.

Furthermore, it is challenging to achieve high accuracy and reliability in diverse and dynamic environments, such as outdoor settings or cluttered indoor spaces. The integration and interoperability of SLAM systems with existing hardware and software platforms. Numerous industries, including robotics, automotive, and augmented reality, rely on a diverse array of hardware components and software frameworks. It can be difficult and require extensive customization and development efforts to ensure seamless integration and compatibility between SLAM solutions and these existing platforms. Furthermore, interoperability concerns among diverse SLAM systems and standards may pose obstacles to collaboration and hinder the scalability of SLAM-based applications across diverse industries.

Privacy and security concerns associated with SLAM technology pose challenges, especially in applications involving sensitive data or environments. Since SLAM systems rely on sensors such as cameras and LIDAR to collect and process data about physical spaces, there are concerns about potential privacy breaches and unauthorized access to sensitive information. Addressing these concerns and adopting robust security measures to protect data privacy and integrity are essential for fostering trust and adoption of SLAM technology.

Category-Wise Acumens

Will Increase in the Production of UAVs Boost the Growth of the Market?

According to VMR analysis, the escalating utilization of unmanned Aerial Vehicles (UAVs), commonly referred to as drones, is presently poised to significantly impact the expansion of enterprises operating in diverse industries. UAVs provide numerous advantages across various industries, including enhanced operational efficacy, cost reduction, enhanced safety, and access to remote or hazardous environments. In various industries, such as agriculture, construction, infrastructure inspection, aerial photography, and emergency response, unmanned aerial vehicles (UAVs) provide companies with the opportunity to acquire valuable data, monitor assets, and execute tasks with greater speed, precision, and flexibility.

In agriculture, UAVs equipped with specialized sensors can monitor crop health, assess soil conditions, and optimize irrigation and pesticide application, leading to higher yields and reduced resource usage. In construction and infrastructure, UAVs can perform aerial surveys, monitor construction progress, and inspect structures, improving project planning, monitoring, and maintenance processes while reducing costs and risks associated with manual inspections. In industries such as oil and gas, utilities, and public safety, UAVs can conduct aerial surveillance, monitor pipelines and power lines, and assist in search and rescue operations, enhancing operational efficiency and safety. This surging application of UAVs is bolstering demand for SLAM over the forecast period.

How will Sales of Deep Learning Based SLAM Fare for SLAM Market?

Deep Learning Based Simultaneous Localization and Mapping (SLAM) is experiencing significant growth. Deep learning techniques have revolutionized the field of computer vision, enabling more accurate and robust perception capabilities. Deep learning models can extract meaningful features from sensor data, such as images and point clouds, by leveraging neural networks and large datasets. This allows for more precise localization and mapping in complex environments.

The increasing availability of powerful hardware, such as graphics processing units (GPUs) and specialized accelerators like tensor processing units (TPUs), has facilitated the training and deployment of deep learning models for SLAM applications. These hardware advances enable faster processing of large volumes of sensor data, making real-time SLAM feasible even on resource-constrained devices.

The proliferation of data-driven approaches and open-source frameworks has lowered the barrier to entry for developers and researchers interested in implementing SLAM solutions. The democratization of technology has sparked innovation and collaboration within the SLAM community, resulting in rapid advancements in algorithmic performance and scalability.

Global Simultaneous Localization and Mapping Report Methodology

Country/Region-wise Acumens

Which Region has the Most Potential for Growth in Simultaneous Localization and Mapping?

The Asia-Pacific region presents significant potential for the advancement of Simultaneous Localization and Mapping (SLAM) technology. With the rapid expansion of economies, the escalating urbanization, and the escalating investments in robotics, autonomous vehicles, and augmented reality applications, there is a rising demand for precise and dependable localization and mapping solutions across diverse industries.

Countries such as China, Japan, and South Korea are at the forefront of technological innovation, with thriving ecosystems of research institutions, start-ups, and established companies driving advancements in SLAM algorithms and applications.

Moreover, the extensive manufacturing base and consumer market in the region present ample prospects for the deployment of SLAM-enabled products and services, rendering Asia-Pacific a crucial growth market for SLAM technology.

Which Region is Dominating in Simultaneous Localization and Mapping Market?

North America is emerging as a dominant force within the Simultaneous Localization and Mapping (SLAM) market. This prominence is attributed to several factors. North America has a strong ecosystem of technology companies, research institutions, and start-ups that specialize in robotics, autonomous vehicles, augmented reality, and other SLAM-enabled applications.

Silicon Valley, California, and the Boston area, Massachusetts, are major hubs for innovation and investment in SLAM technology. Furthermore, North America is home to leading players in the automotive industry, who are investing heavily in autonomous driving technology and leveraging SLAM for localization and mapping capabilities.

Favorable government initiatives, supportive regulatory frameworks, and high consumer acceptance of emerging technologies further contribute to North America's dominance in the SLAM market. In general, the region continues to hold a significant position in the research, development, and commercialization of SLAM solutions, rendering it a pivotal player in the global market landscape.

Competitive Landscape

The competitive landscape in global simultaneous localization and mapping markets is dynamic and evolving, driven by changing customer preferences, technological advancements, and market dynamics. Providers continue to innovate and differentiate their offerings to stay competitive and capture market share in this rapidly growing industry.

Some of the prominent players operating in the global simultaneous localization and mapping Market include:

Alphabet

Amazon Robotics

Apple

Microsoft

Clearpath Robotics

Aethon

The Hi-Tech Robotic Systemz

Facebook

Intellias

MAXST

Intel

Magic Leap

Rethink Robotics

Skydio

NavVis

Mobile Industrial Robot Aps

Google

Uber

Sony

Vecna

Locus Robotics

Fetch Robotics

IRobot

LG Electronics

Wikitude

SLAM

DJI

AVIC

Latest Developments:

In October 2020, Apple Inc. acquired Vilynx Inc. Apple's artificial intelligence solutions, which are merged with the iPhone and its applications, strengthened as an outcome of this acquisition.

In February 2020, Facebook, Inc., acquired Scape Technologies Ltd. The acquisition provides Facebook with such a huge number of SLAM-based augmented reality possibilities.

In December 2018, Intel (US) partnered with Waymo (US), an Alphabet subsidiary capable of providing computational power for Level 4 and 5 autonomous vehicles.

In June 2020, OTTO Motors, a Clearpath Robotics division, raised USD 29 million in Series C funding to support the continued growth of its autonomous mobile robot (AMR) platform. This funding was used to increase OTTO's global network of delivery partners and boost its product roadmap for corporate clients, with a focus on the company's industry-leading automation technology.

In May 2020, Kudan Inc has developed KudanSLAM1 in ToF cameras utilizing Analog Devices, K.K. products, as well as the collaborative development of 3D SLAM demonstration software running on ROS. The use of ToF cameras in independent robotics enables 3D SLAM to function even in dimly lit environments where standalone RGB cameras are ineffective.

TABLE OF CONTENTS

1. INTRODUCTION OF GLOBAL SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) MARKET

• 1.1. Overview of the Market
• 1.2. Scope of Report
• 1.3. Assumptions

2. EXECUTIVE SUMMARY

3. RESEARCH METHODOLOGY OF VERIFIED MARKET RESEARCH

• 3.1. Data Mining
• 3.2. Validation
• 3.3. Primary Interviews
• 3.4. List of Data Sources

4. GLOBAL SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) MARKET OUTLOOK

• 4.1. Overview
• 4.2. Market Dynamics
  • 4.2.1. Drivers
  • 4.2.2. Restraints
  • 4.2.3. Opportunities
• 4.3. Porters Five Force Model
• 4.4. Value Chain Analysis

5. GLOBAL SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) MARKET, BY PRODUCT

• 5.1. Overview
• 5.2. Sparse and Dense Methods
• 5.3. Direct and Indirect Methods

6. GLOBAL SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) MARKET, BY APPLICATION

• 6.1. Overview
• 6.2. Mobile Robots
• 6.3. Smart AR
• 6.4. Other

7. GLOBAL SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) MARKET, BY GEOGRAPHY

• 7.1 Overview
• 7.2 North America
  • 7.2.1 U.S.
  • 7.2.2 Canada
  • 7.2.3 Mexico
• 7.3 Europe
  • 7.3.1 Germany
  • 7.3.2 U.K.
  • 7.3.3 France
  • 7.3.4 Rest of Europe
• 7.4 Asia Pacific
  • 7.4.1 China
  • 7.4.2 Japan
  • 7.4.3 India
  • 7.4.4 Rest of Asia Pacific
• 7.5 Rest of the World
  • 7.5.1 Latin America
  • 7.5.2 Middle East and Africa

8. GLOBAL SIMULTANEOUS LOCALIZATION AND MAPPING (SLAM) MARKET COMPETITIVE LANDSCAPE

• 8.1. Overview
• 8.2. Company Market Ranking
• 8.3. Key Development Strategies

9. COMPANY PROFILES

• 9.1. Google
  • 9.1.1 Overview
  • 9.1.2 Financial Performance
  • 9.1.3 Product Outlook
  • 9.1.4 Key Developments
• 9.2. Microsoft
  • 9.2.1. Overview
  • 9.2.2. Financial Performance
  • 9.2.3. Product Outlook
  • 9.2.4. Key Developments
• 9.3. Uber
  • 9.3.1. Overview
  • 9.3.2. Financial Performance
  • 9.3.3. Product Outlook
  • 9.3.4. Key Developments
• 9.4. Sony
  • 9.4.1. Overview
  • 9.4.2. Financial Performance
  • 9.4.3. Product Outlook
  • 9.4.4. Key Developments
• 9.5. Clearpath Robotics
  • 9.5.1. Overview
  • 9.5.2. Financial Performance
  • 9.5.3. Product Outlook
  • 9.5.4. Key Developments
• 9.6. Vecna
  • 9.6.1. Overview
  • 9.6.2. Financial Performance
  • 9.6.3. Product Outlook
  • 9.6.4. Key Developments
• 9.7. Locus Robotics
  • 9.7.1. Overview
  • 9.7.2. Financial Performance
  • 9.7.3. Product Outlook
  • 9.7.4. Key Developments
• 9.8. Fetch Robotics
  • 9.8.1. Overview
  • 9.8.2. Financial Performance
  • 9.8.3. Product Outlook
  • 9.8.4. Key Developments
• 9.9. IRobot
  • 9.9.1. Overview
  • 9.9.2. Financial Performance
  • 9.9.3. Product Outlook
  • 9.9.4. Key Developments
• 9.10. LG Electronics
  • 9.10.1. Overview
  • 9.10.2. Financial Performance
  • 9.10.3. Product Outlook
  • 9.10.4. Key Developments

10 KEY DEVELOPMENTS

• 10.1 Product Launches/Developments
• 10.2 Mergers and Acquisitions
• 10.3 Business Expansions
• 10.4 Partnerships and Collaborations

11 Appendix

• 11.1 Related Research