MATLAB | Visual SLAM with MATLAB @MATLAB | Uploaded May 2024 | Updated October 2024, 1 week ago.
Visual simultaneous localization and mapping (SLAM) is a technological process that empowers robots, drones, and other autonomous systems to create maps of an unknown environment while simultaneously pinpointing their position within it. This technology is seen in many different applications, from steering autonomous vehicles through unknown areas, to enhancing robotic interaction, and even creating immersive augmented reality experiences.
Learn about features from Computer Vision Toolbox™ that leverage class objects, streamlining the development and deployment of visual SLAM projects. These new class objects feature real-time capabilities, increasing the pace of user workflows. In addition, these class objects are designed to cater to different hardware types, including monocular, stereo, and RGB-D cameras. With these new features and a new example, Computer Vision Toolbox provides its users with more tools for building the future of visual SLAM.
- What Is Slam?: bit.ly/3xMkd5Y
- Implement Simultaneous Localization and Mapping (SLAM) with MATLAB: youtu.be/XZxpmS0QuHI
- Understanding SLAM Using Pose Graph Optimization | Autonomous Navigation, Part 3: youtu.be/saVZtgPyyJQ
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Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Visual simultaneous localization and mapping (SLAM) is a technological process that empowers robots, drones, and other autonomous systems to create maps of an unknown environment while simultaneously pinpointing their position within it. This technology is seen in many different applications, from steering autonomous vehicles through unknown areas, to enhancing robotic interaction, and even creating immersive augmented reality experiences.
Learn about features from Computer Vision Toolbox™ that leverage class objects, streamlining the development and deployment of visual SLAM projects. These new class objects feature real-time capabilities, increasing the pace of user workflows. In addition, these class objects are designed to cater to different hardware types, including monocular, stereo, and RGB-D cameras. With these new features and a new example, Computer Vision Toolbox provides its users with more tools for building the future of visual SLAM.
- What Is Slam?: bit.ly/3xMkd5Y
- Implement Simultaneous Localization and Mapping (SLAM) with MATLAB: youtu.be/XZxpmS0QuHI
- Understanding SLAM Using Pose Graph Optimization | Autonomous Navigation, Part 3: youtu.be/saVZtgPyyJQ
--------------------------------------------------------------------------------------------------------
Get a free product trial: goo.gl/ZHFb5u
Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
