Visual Simultaneous Localization and Mapping (vSLAM)
Visual simultaneous localization and mapping (vSLAM), refers to the process of calculating the position and orientation of a camera, with respect to its surroundings, while simultaneously mapping the environment. The process uses only visual inputs from the camera. Applications for visual SLAM include augmented reality, robotics, and autonomous driving. For more details, see Implement Visual SLAM in MATLAB.
Functions
Topics
- Stereo Visual Simultaneous Localization and Mapping
Process image data from a stereo camera to build a map of an outdoor environment and estimate the trajectory of the camera.
- Visual Localization in a Parking Lot
Develop a visual localization system using synthetic image data from the Unreal Engine® simulation environment.
- Stereo Visual SLAM for UAV Navigation in 3D Simulation
Develop a visual SLAM algorithm for a UAV equipped with a stereo camera.
- Develop Visual SLAM Algorithm Using Unreal Engine Simulation (Automated Driving Toolbox)
Develop a visual simultaneous localization and mapping (SLAM) algorithm using image data from the Unreal Engine® simulation environment.
- Implement Visual SLAM in MATLAB
Understand the visual simultaneous localization and mapping (vSLAM) workflow and how to implement it using MATLAB.
- Choose SLAM Workflow Based on Sensor Data
Choose the right simultaneous localization and mapping (SLAM) workflow and find topics, examples, and supported features.