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 Data	Features	Topics	Examples	Toolbox	Code 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	Implement Visual SLAM in MATLAB vSLAM	Monocular Visual Simultaneous Localization and Mapping Develop Visual SLAM Algorithm Using Unreal Engine Simulation	Computer Vision Toolbox	Computer Vision Toolbox C/C++ Code Generation Computer Vision Toolbox GPU Code Generation
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	vSLAM	Stereo Visual Simultaneous Localization and Mapping Develop Visual SLAM Algorithm Using Unreal Engine Simulation	Computer Vision Toolbox	Computer Vision Toolbox C/C++ Code Generation Computer Vision Toolbox GPU Code Generation
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	vSLAM	Visual SLAM with RGB-D Camera	Computer Vision Toolbox	Computer Vision Toolbox C/C++ Code Generation Computer Vision Toolbox GPU Code Generation
2-D lidar scans	Occupancy map building Vehicle pose estimation Pose graph optimization SLAM algorithm tuning SLAM Map Builder app	Mapping (Navigation Toolbox) SLAM (Navigation Toolbox) Navigation and Mapping (Lidar Toolbox)	Build Map from 2-D Lidar Scans Using SLAM (Lidar Toolbox) Implement Simultaneous Localization And Mapping (SLAM) with Lidar Scans (Navigation Toolbox) Implement Online Simultaneous Localization And Mapping (SLAM) with Lidar Scans (Navigation Toolbox)	Navigation Toolbox Lidar Toolbox	Navigation Toolbox C/C++ Code Generation (Navigation Toolbox) Navigation Toolbox GPU Code Generation (Navigation Toolbox) Lidar Toolbox C/C++ Code Generation (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	Implement Point Cloud SLAM in MATLAB	Build a Map from Lidar Data Using SLAM Design Lidar SLAM Algorithm Using Unreal Engine Simulation Environment Build Occupancy Map from 3-D Lidar Data Using SLAM (Automated Driving Toolbox)	Computer Vision Toolbox	Computer Vision Toolbox C/C++ Code Generation Computer Vision Toolbox GPU Code Generation
3-D lidar scans	Feature-based: Registration Loop closure detection Localization in a known map	Navigation and Mapping (Lidar Toolbox)	Aerial Lidar SLAM Using FPFH Descriptors (Lidar Toolbox) Build Map and Localize Using Segment Matching (Lidar Toolbox) Feature-Based Map Building from Lidar Data (Lidar Toolbox) Build a Map with Lidar Odometry and Mapping (LOAM) Using Unreal Engine Simulation (Lidar Toolbox)	Lidar Toolbox	Lidar Toolbox C/C++ Code Generation (Lidar Toolbox)

Sensor Data

Features

Topics

Examples

Toolbox

Code Generation

Monocular images