Growth Opportunity Analysis of SLAM Technology in Imaging Applications

Growth Opportunity Analysis of SLAM Technology in Imaging Applications

Mapping, Surveying, and Location-based Services and Applications are Transforming the Industry

RELEASE DATE
14-Dec-2023
REGION
Global
Research Code: DAB6-01-00-00-00
SKU: ES_2023_461
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Description

Simultaneous localization and mapping (SLAM) technology in imaging applications is relying on sensor technologies and software algorithms. SLAM allows autonomous systems such as self-driving cars, robots, and drones to simultaneously build a map of their surroundings, navigate, operate effectively, and estimate their position on that map. It leverages inertial measurement units, camera systems, and LiDAR to collect data in the surrounding environments. By analyzing the sensor data, SLAM algorithms create a map of the environment and estimate position and orientation parameters. Algorithms are complex and computationally expensive, but they have advanced significantly in the last few decades.
SLAM is vital technology for a wide range of imaging applications in robots, augmented reality (AR), virtual reality (VR), and autonomous vehicles, and the market is expanding quickly. Urban planners, construction managers, and surveyors can create 3D maps of large-scale landscapes with ease and with desired accuracy through SLAM.
This study includes:
•     An overview of SLAM technology in imaging applications
•     Types of SLAM algorithms for imaging application
•     A comparative analysis of SLAM algorithms for imaging applications
•     The impact and significance of sensor fusion in SLAM-based imaging systems
•     An impact analysis of SLAM technology in medical imaging, satellite, robotics, and AR/VR applications
•     Emerging use cases of SLAM technology across industry verticals
•     Key growth opportunities, business models, and future directions

Table of Contents

Why Is It Increasingly Difficult to Grow?

The Strategic Imperative 8™

The Impact of the Top 3 Strategic Imperatives on the Simultaneous Localization and Mapping (SLAM) Industry

Growth Opportunities Fuel the Growth Pipeline Engine™

Research Methodology

Growth Environment

Technology Attractiveness Dashboard

Technology Attractiveness Dashboard (continued)

Technology Attractiveness Dashboard (continued)

Scope of Analysis

Segmentation of SLAM Algorithms for Imaging Applications

Growth Drivers

Growth Restraints

Overview of SLAM Technology in Imaging Applications

Types of SLAM Algorithms for Imaging Applications

Advantages and Disadvantages of Various SLAM Algorithms

Factors to Consider When Choosing a SLAM Algorithm for an Imaging Application

Comparative Analysis of SLAM Algorithms for Imaging Applications

Sensor Fusion for SLAM

SLAM Performance Metrics—Preparation, Evaluation, and Testing Phases

Patent Research Focus Areas

Funding Deals and Focus Areas

Impact Analysis of SLAM Technology for Satellite and Medical Imaging Applications

Emerging Use Cases—SLAM Technology for Satellite and Medical Imaging

SLAM Technology for Robotics Applications

Impact Analysis of SLAM Technology for Robotics Applications

Emerging Use Cases—SLAM Technology for Robotics

SLAM Technology for AR/VR Applications

Impact Analysis of SLAM Technology for AR/VR Applications

Emerging Use Cases—SLAM Technology for AR/VR

Regional Trends and Impact of SLAM Technology

Key Technology Developments of Major Stakeholders and Emerging Start-ups

Various Business Models for SLAM Technology in Imaging Applications

Future Directions of SLAM R&D

Growth Opportunity 1: Multisensor Fusion SLAM Systems

Growth Opportunity 1: Multisensor Fusion SLAM Systems (continued)

Growth Opportunity 2: Autonomous Industrial Robots

Growth Opportunity 2: Autonomous Industrial Robots (continued)

Growth Opportunity 3: UAVs

Growth Opportunity 3: UAVs (continued)

Technology Readiness Levels (TRL): Explanation

Your Next Steps

Why Frost, Why Now?

Legal Disclaimer

Simultaneous localization and mapping (SLAM) technology in imaging applications is relying on sensor technologies and software algorithms. SLAM allows autonomous systems such as self-driving cars, robots, and drones to simultaneously build a map of their surroundings, navigate, operate effectively, and estimate their position on that map. It leverages inertial measurement units, camera systems, and LiDAR to collect data in the surrounding environments. By analyzing the sensor data, SLAM algorithms create a map of the environment and estimate position and orientation parameters. Algorithms are complex and computationally expensive, but they have advanced significantly in the last few decades. SLAM is vital technology for a wide range of imaging applications in robots, augmented reality (AR), virtual reality (VR), and autonomous vehicles, and the market is expanding quickly. Urban planners, construction managers, and surveyors can create 3D maps of large-scale landscapes with ease and with desired accuracy through SLAM. This study includes: An overview of SLAM technology in imaging applications Types of SLAM algorithms for imaging application A comparative analysis of SLAM algorithms for imaging applications The impact and significance of sensor fusion in SLAM-based imaging systems An impact analysis of SLAM technology in medical imaging, satellite, robotics, and AR/VR applications Emerging use cases of SLAM technology across industry verticals Key growth opportunities, business models, and future directions
More Information
Author Varun B
Industries Electronics and Sensors
No Index No
Is Prebook No
Keyword 1 SLAM Technology Analysis
Keyword 2 SLAM Technology Adoption
Keyword 3 Imaging Industry Insights
Podcast No
WIP Number DAB6-01-00-00-00