Visual SLAM and Structure from Motion

Stereo vision, triangulation, 3-D reconstruction, visual simultaneous localization and mapping (vSLAM), and visual-inertial sensor fusion

Stereo vision is the process of recovering depth from camera images by comparing two or more views of the same scene. Structure from Motion (SfM) is the process of estimating the 3-D structure of a scene from a set of 2-D images. Visual simultaneous localization and mapping (vSLAM) is the process of calculating the position and orientation of a camera, with respect to its surroundings, while simultaneously mapping the environment. Visual-inertial SLAM (viSLAM) is the process of fusing visual inputs from a camera with positional data from an IMU to improve the SLAM the results. For more details, see Implement Visual SLAM in MATLAB and What Is Structure from Motion?

Highlighted Topics

Categories

  • Stereo Vision
    Stereo rectification, disparity, and dense 3-D reconstruction
  • vSLAM
    Monocular, stereo, RGB-D visual simultaneous localization and mapping (vSLAM), and visual-inertial sensor fusion
  • Structure from Motion
    3-D reconstruction from multiple views

Featured Examples

Performant and Deployable Monocular Visual SLAM

Performant and Deployable Monocular Visual SLAM

Use visual inputs from a camera to perform vSLAM and generate multi-threaded C/C++ code.

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Performant Monocular Visual-Inertial SLAM

Performant Monocular Visual-Inertial SLAM

Use visual inputs from a camera and positional data from an IMU to perform viSLAM in real time.

Augmented Reality Using AprilTag Markers

Augmented Reality Using AprilTag Markers

Use marker-based augmented reality to render virtual content into a scene.

Performant and Deployable Stereo Visual SLAM with Fisheye Images

Performant and Deployable Stereo Visual SLAM with Fisheye Images

Use fisheye image data from a stereo camera to perform VSLAM and generate multi-threaded C/C++ code.

Depth Estimation from Stereo Video

Depth Estimation from Stereo Video

Detect people in video taken with a calibrated stereo camera and determine distance from the camera.

Uncalibrated Stereo Image Rectification

Uncalibrated Stereo Image Rectification

Compute the rectification of two uncalibrated images.

Develop Visual SLAM Algorithm Using Unreal Engine Simulation

Develop Visual SLAM Algorithm Using Unreal Engine Simulation

Develop a visual simultaneous localization and mapping (SLAM) algorithm using image data obtained from the Unreal Engine® simulation environment.

Visual SLAM with RGB-D Camera

Visual SLAM with RGB-D Camera

Process RGB-D image data to build a map of an indoor environment and estimate the trajectory of the camera.

Monocular Visual-Inertial Odometry (VIO) Using Factor Graph

Monocular Visual-Inertial Odometry (VIO) Using Factor Graph

Perform monocular visual-inertial odometry using tightly coupled factor graph optimization.