SLAM

R2026b
2차원 및 3차원 동시적 위치추정 및 지도작성

SLAM(동시적 위치추정 및 지도작성)은 맵 작성 및 위치추정 알고리즘 항목을 모두 사용하여 맵을 작성하는 동시에 해당 맵 내에서 이동체의 위치를 추정합니다. lidarSLAM을 사용하면 라이다 스캔과 오도메트리 자세 추정값을 처리하는 사용자 자신의 SLAM 알고리즘을 조정하여 맵을 반복적으로 작성할 수 있습니다. buildMap을 사용하면 기록되고 필터링된 데이터를 취하고 SLAM을 사용해 맵을 만들 수 있습니다. SLAM 맵 작성기 앱을 사용하면 관련 자세를 수동으로 수정하고 스캔을 정렬하여 맵의 정확도를 개선할 수 있습니다.

SLAM이 무엇인지, 그리고 다른 MATLAB® 툴박스의 SLAM 툴에 대해 자세히 알아보려면 SLAM이란?을 참조하십시오.

앱

SLAM 맵 작성기라이다 기반 SLAM을 사용하여 2차원 그리드 맵 작성

함수

모두 확장

monovslamVisual simultaneous localization and mapping (vSLAM) and visual-inertial sensor fusion with monocular camera (R2025a 이후)
rgbdvslamFeature-based visual simultaneous localization and mapping (vSLAM) and visual-inertial sensor fusion with RGB-D camera (R2025a 이후)
stereovslamFeature-based visual simultaneous localization and mapping (vSLAM) and visual-inertial sensor fusion with stereo camera (R2025a 이후)
ekfSLAMPerform simultaneous localization and mapping using extended Kalman filter
lidarSLAM Perform localization and mapping using lidar scans
buildMap라이다 스캔을 이용해 점유 맵 작성하기
factorGraphGraph-based framework for multisensor state estimation (R2022a 이후)
importFactorGraphImport factor graph from g2o log file (R2022a 이후)
factorIMUConvert IMU readings to factor (R2022a 이후)
factorGPSFactor for GPS measurement (R2022a 이후)
factorTwoPoseSE2Factor relating two SE(2) poses (R2022a 이후)
factorTwoPoseSE3Relative pose factor connecting two SE(3) poses (R2022a 이후)
factorTwoPoseSIM3Factor relating two SIM(3) poses (R2025a 이후)
factorPoseSE2AndPointXYFactor relating SE(2) position and 2-D point (R2022b 이후)
factorPoseSE3AndPointXYZFactor relating SE(3) position and 3-D point (R2022b 이후)
factorIMUBiasPriorPrior factor for IMU bias (R2022a 이후)
factorVelocity3PriorPrior factor for 3-D velocity (R2022a 이후)
factorPoseSE3PriorFull-state prior factor for SE(3) pose (R2022a 이후)
factorTransformSE3PriorFull-state prior factor for SE(3) transform (R2026a 이후)
factorCameraSE3AndPointXYZFactor relating SE(3) camera pose, 3-D landmark point, and optional sensor transform (R2023a 이후)
estimateGravityRotationEstimate gravity rotation using factor graph optimization (R2023a 이후)
estimateGravityRotationAndPoseScaleEstimate gravity rotation and pose scale using factor graph optimization (R2023a 이후)
estimateCameraIMUTransformEstimate transformation from camera to IMU sensor using calibration data (R2024a 이후)
poseGraph 2차원 자세 그래프 만들기
poseGraph3D 3차원 자세 그래프 만들기
poseGraphSolverOptionsSolver options for pose graph optimization
optimizePoseGraphOptimize nodes in pose graph
trimLoopClosuresOptimize pose graph and remove bad loop closures
poseplot3차원 자세 플롯

도움말 항목

추천 예제

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.

Factor Graph-Based Pedestrian Localization with IMU and GPS Sensors

Factor Graph-Based Pedestrian Localization with IMU and GPS Sensors

Estimate the position of a pedestrian using logged sensor data from an inertial measurement unit (IMU) and Global Positioning System (GPS) receiver and 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.

새로 만들기
Map Large Environments Using Multi-Agent Collaborative SLAM

Map Large Environments Using Multi-Agent Collaborative SLAM

Map a large environment using multiple stereo-camera agents that independently run visual SLAM, detect shared locations via visual place recognition, and merge their maps through pose graph optimization.

(Computer Vision Toolbox)

  • R2026b 이후
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.

라이다 스캔으로 SLAM(동시적 위치추정 및 지도작성) 구현하기

라이다 스캔으로 SLAM(동시적 위치추정 및 지도작성) 구현하기

자세 그래프와 일련의 라이다 스캔 컬렉션을 사용하여 오프라인 SLAM을 구현하고 환경 맵을 작성합니다.