SLAM

2-D and 3-D simultaneous localization and mapping

Simultaneous localization and mapping (SLAM) uses both Mapping and Localization and Pose Estimation algorithms to build a map and localize your vehicle in that map at the same time. Use lidarSLAM to tune your own SLAM algorithm that processes lidar scans and odometry pose estimates to iteratively build a map. Use buildMap to take logged and filtered data to create a map using SLAM. The SLAM Map Builder app lets you manually modify relative poses and align scans to improve the accuracy of your map.

Apps

SLAM Map BuilderBuild 2-D grid maps using lidar-based SLAM

Objects

lidarSLAM Perform localization and mapping using lidar scans
poseGraph Create 2-D pose graph
poseGraph3D Create 3-D pose graph

Functions

buildMapBuild occupancy map from lidar scans
addScanAdd scan to lidar SLAM map
addRelativePoseAdd relative pose to pose graph
optimizePoseGraphOptimize nodes in pose graph
removeLoopClosures Remove loop closures from pose graph
scansAndPoses Extract scans and corresponding poses

Featured Examples

Perform SLAM Using 3-D Lidar Point Clouds

Perform SLAM Using 3-D Lidar Point Clouds

Demonstrates how to implement the Simultaneous Localization And Mapping (SLAM) algorithm on collected 3-D lidar sensor data using point cloud processing algorithms and pose graph optimization. The goal of this example is to estimate the trajectory of the robot and create a 3-D occupancy map of the environment from the 3-D lidar point clouds and estimated trajectory.

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Implement Simultaneous Localization And Mapping (SLAM) with Lidar Scans

Implement Simultaneous Localization And Mapping (SLAM) with Lidar Scans

Demonstrates how to implement the Simultaneous Localization And Mapping (SLAM) algorithm on a collected series of lidar scans using pose graph optimization. The goal of this example is to build a map of the environment using the lidar scans and retrieve the trajectory of the robot.

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Implement Online Simultaneous Localization And Mapping (SLAM) with Lidar Scans

Implement Online Simultaneous Localization And Mapping (SLAM) with Lidar Scans

Demonstrates how to implement the Simultaneous Localization And Mapping (SLAM) algorithm on lidar scans obtained from simulated environment using pose graph optimization. This example requires Simulink® 3D Animation™ and Navigation Toolbox™.

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