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.

For more information about what SLAM is and other SLAM tools in other MATLAB® toolboxes, see What is SLAM?.

Apps

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

Functions

expand all

monovslamVisual simultaneous localization and mapping (vSLAM) and visual-inertial sensor fusion with monocular camera (Since R2025a)
rgbdvslamFeature-based visual simultaneous localization and mapping (vSLAM) and visual-inertial sensor fusion with RGB-D camera (Since R2025a)
stereovslamFeature-based visual simultaneous localization and mapping (vSLAM) and visual-inertial sensor fusion with stereo camera (Since R2025a)
ekfSLAMPerform simultaneous localization and mapping using extended Kalman filter (Since R2021b)
lidarSLAM Perform localization and mapping using lidar scans
buildMapBuild occupancy map from lidar scans
poseGraph Create 2-D pose graph
poseGraph3D Create 3-D pose graph
poseGraphSolverOptionsSolver options for pose graph optimization (Since R2020b)
optimizePoseGraphOptimize nodes in pose graph
trimLoopClosuresOptimize pose graph and remove bad loop closures (Since R2020b)
poseplot3-D pose plot (Since R2021b)
factorGraphGraph-based framework for multisensor state estimation (Since R2022a)
importFactorGraphImport factor graph from g2o log file (Since R2022a)
factorIMUConvert IMU readings to factor (Since R2022a)
factorGPSFactor for GPS measurement (Since R2022a)
factorTwoPoseSE2Factor relating two SE(2) poses (Since R2022a)
factorTwoPoseSE3Factor relating two SE(3) poses (Since R2022a)
factorTwoPoseSIM3Factor relating two SIM(3) poses (Since R2025a)
factorPoseSE2AndPointXYFactor relating SE(2) position and 2-D point (Since R2022b)
factorPoseSE3AndPointXYZFactor relating SE(3) position and 3-D point (Since R2022b)
factorIMUBiasPriorPrior factor for IMU bias (Since R2022a)
factorVelocity3PriorPrior factor for 3-D velocity (Since R2022a)
factorPoseSE3PriorFull-state prior factor for SE(3) pose (Since R2022a)
factorCameraSE3AndPointXYZFactor relating SE(3) camera pose, 3-D landmark point, and optional sensor transform (Since R2023a)
estimateGravityRotationEstimate gravity rotation using factor graph optimization (Since R2023a)
estimateGravityRotationAndPoseScaleEstimate gravity rotation and pose scale using factor graph optimization (Since R2023a)
estimateCameraIMUTransformEstimate transformation from camera to IMU sensor using calibration data (Since R2024a)

Topics

Featured Examples

Performant and Deployable Monocular Visual SLAM

Performant and Deployable Monocular Visual SLAM

Visual simultaneous localization and mapping (vSLAM) refers to the process of calculating the position and orientation of a camera with respect to its surroundings while simultaneously mapping the environment. Applications for vSLAM include augmented reality, robotics, and autonomous driving. In this example, the algorithm uses only visual inputs from the camera.

Build a Map from Lidar Data Using SLAM

Build a Map from Lidar Data Using SLAM

Process 3-D lidar data from a sensor on a vehicle to progressively build a map and estimate the trajectory using SLAM.

Monocular Visual-Inertial Odometry (VIO) Using Factor Graph

Monocular Visual-Inertial Odometry (VIO) Using Factor Graph

Implement monocular visual-inertial odometry to localize a UAV using camera and IMU data, optimized by a factor graph.

Landmark SLAM Using AprilTag Markers

Landmark SLAM Using AprilTag Markers

Combine robot odometry data and AprilTag markers to implement SLAM with a factor graph and pose graph, and compare the results.

Map Indoor Area Using Lidar SLAM and Factor Graph

Map Indoor Area Using Lidar SLAM and Factor Graph

Implement offline lidar SLAM on collected lidar scans to map an indoor area using a factor graph and lidar scan map.

Build and Deploy Visual SLAM Algorithm with ROS in MATLAB

Build and Deploy Visual SLAM Algorithm with ROS in MATLAB

Implement a monocular visual SLAM algorithm to estimate camera poses and deploy generated C++ code using ROS.

Simulate RGB-D Visual SLAM System with Cosimulation in Gazebo and Simulink

Simulate RGB-D Visual SLAM System with Cosimulation in Gazebo and Simulink

Use RGB and depth images from a robot to simulate a RGB-D visual SLAM system in Simulink.

EKF-Based Landmark SLAM

EKF-Based Landmark SLAM

Implement landmark SLAM using the Extended Kalman Filter algorithm to track the path of a vehicle and map the surroundings.

Implement Simultaneous Localization And Mapping (SLAM) with Lidar Scans

Implement Simultaneous Localization And Mapping (SLAM) with Lidar Scans

Implement offline SLAM using a pose graph and a collection series of lidar scans, and build a map of the environment.