Main Content

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 What is SLAM?.

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

2-D lidar scans

  • Occupancy map building

  • Vehicle pose estimation

  • Pose graph optimization

  • SLAM algorithm tuning

  • SLAM Map Builder app

  • 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

  • Computer Vision Toolbox

3-D lidar scans

Feature-based:

  • Registration

  • Loop closure detection

  • Localization in a known map

  • Lidar Toolbox