SLAM

2-D and 3-D simultaneous localization and mapping

Simultaneous localization and mapping (SLAM) uses bothMappingand本地化和姿势估计algorithms to build a map and localize your vehicle in that map at the same time. UselidarSLAMto tune your own SLAM algorithm that processes lidar scans and odometry pose estimates to iteratively build a map. UsebuildMapto take logged and filtered data to create a map using SLAM. TheSLAM Map Builderapp lets you manually modify relative poses and align scans to improve the accuracy of your map.

Apps

SLAM Map Builder

Build 2-D grid maps using lidar-based SLAM

Functions

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ekfslam

`ekfslam`	使用扩展的卡尔曼过滤器同时进行本地化和映射
`correct`	Correct state and state error covariance
`landmarkInfo`	Retrieve landmark information
`poseHistory`	Retrieve corrected and predicted pose history
`预测`	Predict state and state error covariance
`removeLandmark`	从国家矢量中删除里程碑
`reset`	重置状态和州估计错误协方差

lidarSLAM

`lidarSLAM`	使用激光雷达扫描执行本地化和映射
`addScan`	Add scan to lidar SLAM map
`buildMap`	Build occupancy map from lidar scans
`删除`	从姿势图中删除循环封闭
`扫描`	Extract scans and corresponding poses
`show`	Plot scans and robot poses

Featured Examples

EKF-Based Landmark SLAM

Use ekfSLAM for a reliable implementation of landmark Simultaneous Localization and Mapping (SLAM) using the Extended Kalman Filter (EKF) algorithm and maximum likelihood algorithm for data association. In this example, you create a landmark map of the immediate surroundings of a vehicle and simultaneously track the path of the vehicle. Generate a trajectory by moving the vehicle using the noisy control commands, and form the map using the landmarks it encounters along the path. Correct the vehicle trajectory and landmark estimates by observing the landmarks again.

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Landmark SLAM Using AprilTag Markers

Combine robot odometry data and observed fiducial markers called AprilTags to better estimate the robot trajectory and the landmark positions in the environment.

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Perform SLAM Using 3-D Lidar Point Clouds

演示如何使用点云处理算法和姿势图优化实现收集的3-D激光雷达传感器数据上的同时定位和映射（SLAM）算法。此示例的目的是估计机器人的轨迹，并从3-D LIDAR点云和估计的轨迹创建环境的3-D占用图。

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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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通过激光扫描实施在线同时本地化和映射（SLAM）

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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