2024 年至 2031 年按地圖類型、產品、應用、最終用戶和地區劃分的同步定位和地圖繪製 (SLAM) 市場

2024 年至 2031 年同步定位與地圖繪製 (SLAM) 市場評估

同步定位和地圖繪製是一種使設備或機器人能夠即時瞭解和繪製其環境，同時確定其自身在該環境中的位置的技術。這使得軍事和國防、製造業和許多其他領域的應用非常有效率。據 Verified Market Research 分析師稱，2023 年全球同步定位和地圖繪製市值將達到 2.62 億美元。預計到 2031 年收入將達到 18 億美元。

市場擴張歸因於多種因素，包括對 AR/VR 應用的需求不斷增加、自動駕駛汽車的普及以及感測器技術的進步。 SLAM 應用的激增將推動市場在 2024 年至 2031 年期間以 41.6% 的複合年增長率成長。

同步定位和地圖繪製 (SLAM) 市場：定義/概述

定位與繪圖同時進行是藉助無人駕駛車輛或機器人在環境中導航來創建地圖的過程。 SLAM 是一種用於機器人製圖或機器人測繪的系統。該過程涉及使用複雜的計算、演算法和感官輸入進行導航。這使得可以遠端建立地理資訊系統 (GIS) 數據，即使在人類無法繪製地圖的危險環境中也是如此。開發或升級地圖時遇到的計算困難稱為同時定位和地圖繪製。

專為SLAM應用而設計的機器人稱為SLAM機器人。 SLAM（同步定位和地圖建構）是機器人和無人駕駛汽車採用的技術，用於同時生成地圖並使用它來導航其環境。由於視覺 SLAM 系統需要即時運行，因此它們會定期分別對位置和映射資料進行捆綁調整，但同時進行以加快最終的整合。 SLAM 技術有許多應用，包括擴增實境、虛擬影像投影和廣泛的現場機器人技術。同步定位和映射技術顯著提高了準確性。

哪些因素推動了全球 SLAM（同步定位和地圖繪製）市場的發展？

全球 SLAM 市場受到推動其採用和成長的幾個關鍵因素的驅動。一個關鍵因素是各行各業對自主移動機器人和車輛的需求不斷增長。這些機器人和車輛依靠 SLAM 技術來精確導航和繪製周圍環境，無需人工幹預。

隨著製造業、物流業和農業等行業自動化程度的提高，對強大的 SLAM 解決方案的需求也日益增加。擴增實境（AR）和虛擬實境（VR）應用越來越受歡迎。 SLAM 技術透過即時精確追蹤使用者的位置和周圍環境，在實現沉浸式 AR 體驗方面發揮關鍵作用。

在虛擬實境應用中，SLAM 透過映射實體空間和無縫整合數位內容來促進可信任的虛擬環境的創建。遊戲、娛樂、教育和企業應用中 AR 和 VR 的使用情況日益增多，推動了對先進 SLAM 解決方案的需求。

此外，感測器技術的進步，特別是光達、攝影系統和慣性感測器領域的進步，顯著提高了 SLAM 演算法的準確性和可靠性。這些技術進步正在推動能夠在各種環境和課題中運行的更強大和高效的 SLAM 系統的發展。因此，機器人、汽車和家電等各個行業都面臨著越來越大的課題，需要將 SLAM 技術融入他們的產品和服務中，以提高其性能和功能。

哪些問題導致SLAM銷售暴跌？

儘管機會光明，但全球 SLAM 市場仍面臨著一些可能阻礙其採用和成長的課題。 SLAM 演算法的複雜性和計算嚴謹性，尤其是對於即時應用。開發一個能夠準確繪製環境地圖並即時追蹤位置同時有效管理運算資源的強大 SLAM 系統仍然是一個技術障礙。

另一個課題是在多樣化和動態環境中實現高精度和可靠性，例如戶外和雜亂的室內空間。 SLAM 系統與現有硬體和軟體平台的整合和互通性。包括機器人、汽車和擴增實境在內的許多行業都依賴各種各樣的硬體組件和軟體框架。確保 SLAM 解決方案與這些現有平台的無縫整合和相容性可能很困難，並且需要大量的客製化和開發工作。此外，不同 SLAM 系統和標準之間的互通性問題可能會對協作造成障礙，並阻礙基於 SLAM 的應用程式在不同產業中的可擴展性。

與 SLAM 技術相關的隱私和安全問題帶來了課題，尤其是在涉及敏感資料或環境的應用中。由於 SLAM 系統依靠攝影機和光達等感測器來收集和處理有關物理空間的數據，因此人們擔心潛在的隱私侵犯和未經授權存取敏感資訊。解決這些問題並採用強大的安全措施來保護資料的隱私和完整性對於培養對 SLAM 技術的信任和採用至關重要。

目錄

第 1 章 全球 SLAM（同步定位與地圖建構）市場：簡介

市場概況
• 研究範圍
• 先決條件

第 2 章執行摘要

第 3 章：經過驗證的市場研究方法

• 資料探勘
• 驗證
• 主要來源
• 資料來源列表

第四章 全球 SLAM（同步定位與地圖建構）市場展望

• 概述
• 市場動態
  • 驅動程式
  • 阻礙因素
  • 機會
• 波特五力模型
• 價值鏈分析

第 5 章 全球同步定位與地圖繪製 (SLAM) 市場（按產品）

• 概述
• 稀疏方法與密集方法
• 直接法與間接法

第六章 全球同步定位和地圖繪製 (SLAM) 市場（按應用）

• 概述
• 移動機器人
• 智慧擴增實境
• 其他

第 7 章。

• 概述
• 北美洲
  • 美國
  • 加拿大
  • 墨西哥
• 歐洲
  • 德國
  • 英國
  • 法國
  • 其他歐洲國家
  亞太地區
  • 中國
  • 日本
  • 印度
  • 其他亞太地區
• 世界其他地區
  • 拉丁美洲
  • 中東和非洲

第 8 章。

• 概述
• 各公司的市場排名
• 主要發展策略

第九章 公司簡介

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

第 10 章 重大進展

• 產品發佈/開發
• 合併和收購
• 業務擴展
• 夥伴關係和合作關係

第 11 章附錄

• 相關研究

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