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.
Detect, Extract, and Match Features
Reconstruct 3-D Structure
|Estimate 2-D geometric transformation from matching point pairs|
|Estimate 3-D geometric transformation from matching point pairs|
|Estimate fundamental matrix from corresponding points in stereo images|
|Estimate camera pose from 3-D to 2-D point correspondences|
|Find world points observed in view|
|Find world points that correspond to point tracks|
|Compute relative rotation and translation between camera poses|
Optimize Motion and 3-D Structure
|Optimize absolute poses using relative pose constraints|
|Create pose graph|
|Adjust collection of 3-D points and camera poses|
|Adjust collection of 3-D points and camera poses using motion-only bundle adjustment|
|Refine 3-D points using structure-only bundle adjustment|
- 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.