Mapping, Surveying, and Location-based Services and Applications are Transforming the Industry
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
- Strategic Imperatives
- 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
Growth Opportunity Analysis
- Scope of Analysis
- Segmentation of SLAM Algorithms for Imaging Applications
- Growth Drivers
- Growth Restraints
Technology Landscape Assessment
- 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 Imaging Application Across Industry Verticals
- 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 Universe
- Growth Opportunity 1: Multisensor Fusion SLAM Systems
- Growth Opportunity 2: Autonomous Industrial Robots
- Growth Opportunity 3: UAVs
Appendix
- Technology Readiness Levels (TRL): Explanation
Next Steps
- Your Next Steps
- Why Now?
- Legal Disclaimer
