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choose slam workflow based on sensor data

you can use computer vision toolbox™, navigation toolbox™, and lidar toolbox™ for simultaneous localization and mapping (slam). slam is widely used in applications including automated driving, robotics, and unmanned aerial vehicles (uav). to learn more about slam, see .

choose slam workflow

to choose the right slam workflow for your application, consider what type of sensor data you are collecting. matlab® support slam workflows that use images from a monocular or stereo camera system, or point cloud data including 2-d and 3-d lidar data.

this table summarizes the key features available for slam.

sensor datafeaturestopicsexamplestoolboxcode generation

monocular images

  • feature detection, extraction, and matching

  • triangulation and bundle adjustment

  • data management for key frames and map points

  • loop closure detection using bag of features

  • similarity pose graph optimization

  • computer vision toolbox

stereo images

  • stereo image rectification

  • feature detection, extraction, and matching

  • reconstruction from disparity, triangulation, and bundle adjustment

  • data management for key frames and map points

  • loop closure detection using bag of features

  • pose graph optimization

  • computer vision toolbox

rgb-d images

  • feature detection, extraction, and matching

  • reconstruction from depth images, triangulation, and bundle adjustment

  • data management for key frames and map points

  • loop closure detection using bag of features

  • pose graph optimization

  • computer vision toolbox

2-d lidar scans

  • occupancy map building

  • vehicle pose estimation

  • pose graph optimization

  • slam algorithm tuning

  • slam map builder app

  • navigation toolbox

  • lidar toolbox

  • (navigation toolbox)

  • (navigation toolbox)

  • (lidar toolbox)

point cloud data

  • point cloud processing

  • registration

  • data management for map building

  • loop closure detection with global features

  • pose graph optimization

  • localization in a known map

  • (automated driving toolbox)

  • computer vision toolbox

3-d lidar scans

feature-based:

  • registration

  • loop closure detection

  • localization in a known map

  • (lidar toolbox)

  • (lidar toolbox)

  • (lidar toolbox)

  • (lidar toolbox)

  • lidar toolbox

  • (lidar toolbox)

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