위치추정 알고리즘

입자 필터, 스캔 매칭, 몬테카를로 위치추정, 자세 그래프, 오도메트리

몬테카를로 위치추정과 같은 위치추정 알고리즘과 스캔 매칭은 거리 센서 또는 라이다 측정값을 사용하여 알려진 맵에서 자세를 추정합니다. 자세 그래프는 추정된 자세를 추적하며 간선 제약 조건과 루프 폐쇄를 기반으로 최적화될 수 있습니다. 동시적 위치추정 및 지도작성에 대한 내용은 SLAM 항목을 참조하십시오.

함수

모두 확장

입자 필터 상태 추정

`stateEstimatorPF`	Create particle filter state estimator
`resamplingPolicyPF`	Create resampling policy object with resampling settings (R2019b 이후)

스캔 매칭

`matchScans`	Estimate pose between two laser scans (R2019b 이후)
`matchScansGrid`	Estimate pose between two lidar scans using grid-based search (R2019b 이후)
`matchScansLine`	Estimate pose between two laser scans using line features (R2020a 이후)
`transformScan`	Transform laser scan based on relative pose
`lidarScan`	2차원 라이다 스캔을 저장하기 위한 객체 생성 (R2019b 이후)

몬테카를로 위치추정

`monteCarloLocalization`	Localize robot using range sensor data and map (R2019b 이후)
`lidarScan`	2차원 라이다 스캔을 저장하기 위한 객체 생성 (R2019b 이후)
`odometryMotionModel`	Create an odometry motion model (R2019b 이후)
`likelihoodFieldSensorModel`	Create likelihood field range sensor model (R2019b 이후)

자세 그래프

`poseGraph`	2차원 자세 그래프 만들기 (R2019b 이후)
`poseGraph3D`	3차원 자세 그래프 만들기 (R2019b 이후)
`poseGraphSolverOptions`	Solver options for pose graph optimization (R2020b 이후)
`optimizePoseGraph`	Optimize nodes in pose graph (R2019b 이후)
`trimLoopClosures`	Optimize pose graph and remove bad loop closures (R2020b 이후)
`poseplot`	3-D pose plot (R2021b 이후)

인자 그래프

`factorGraph`	Bipartite graph of factors and nodes (R2022a 이후)
`importFactorGraph`	Import factor graph from g2o log file (R2022a 이후)
`factorIMU`	Convert IMU readings to factor (R2022a 이후)
`factorGPS`	Factor for GPS measurement (R2022a 이후)
`factorTwoPoseSE2`	Factor relating two SE(2) poses (R2022a 이후)
`factorTwoPoseSE3`	Factor relating two SE(3) poses (R2022a 이후)
`factorPoseSE2AndPointXY`	Factor relating SE(2) position and 2-D point (R2022b 이후)
`factorPoseSE3AndPointXYZ`	Factor relating SE(3) position and 3-D point (R2022b 이후)
`factorIMUBiasPrior`	Prior factor for IMU bias (R2022a 이후)
`factorVelocity3Prior`	Prior factor for 3-D velocity (R2022a 이후)
`factorPoseSE3Prior`	Full-state prior factor for SE(3) pose (R2022a 이후)
`factorCameraSE3AndPointXYZ`	Factor relating SE(3) camera pose and 3-D point (R2023a 이후)
`estimateGravityRotation`	Estimate gravity rotation using IMU measurements and factor graph optimization (R2023a 이후)
`estimateGravityRotationAndPoseScale`	Estimate gravity rotation and pose scale using IMU measurements and factor graph optimization (R2023a 이후)
`estimateCameraIMUTransform`	Estimate transformation from camera to IMU sensor using calibration data (R2024a 이후)

휠 인코더 오도메트리

`wheelEncoderOdometryAckermann`	Compute Ackermann vehicle odometry using wheel encoder ticks and steering angle (R2020b 이후)
`wheelEncoderOdometryBicycle`	Compute bicycle odometry using wheel encoder ticks and steering angle (R2020b 이후)
`wheelEncoderOdometryDifferentialDrive`	Compute differential-drive vehicle odometry using wheel encoder ticks (R2020b 이후)
`wheelEncoderOdometryUnicycle`	Compute unicycle odometry using wheel encoder ticks and angular velocity (R2020b 이후)

도움말 항목

Compose a Series of Laser Scans with Pose Changes
Use the matchScans function to compute the pose difference between a series of laser scans.
Minimize Search Range in Grid-based Lidar Scan Matching Using IMU
This example shows how to use an inertial measurement unit (IMU) to minimize the search range of the rotation angle for scan matching algorithms.
Monte Carlo Localization Algorithm
The Monte Carlo Localization (MCL) algorithm is used to estimate the position and orientation of a robot.
Particle Filter Workflow
A particle filter is a recursive, Bayesian state estimator that uses discrete particles to approximate the posterior distribution of the estimated state.
Particle Filter Parameters
To use the stateEstimatorPF (Robotics System Toolbox) particle filter, you must specify parameters such as the number of particles, the initial particle location, and the state estimation method.

추천 예제

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.

R2023b 이후

라이브 스크립트 열기

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.

라이브 스크립트 열기

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.

라이브 스크립트 열기

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.

라이브 스크립트 열기

Localize TurtleBot Using Monte Carlo Localization Algorithm

Apply the Monte Carlo Localization algorithm on a TurtleBot® robot in a simulated Gazebo® environment.

라이브 스크립트 열기

Reduce Drift in 3-D Visual Odometry Trajectory Using Pose Graphs

Reduce the drift in the estimated trajectory (location and orientation) of a monocular camera using 3-D pose graph optimization. Visual odometry estimates the current global pose of the camera (current frame). Because of poor matching or errors in 3-D point triangulation, robot trajectories often tends to drift from the ground truth. Loop closure detection and pose graph optimization reduce this drift and correct for errors.

라이브 스크립트 열기