本土化

同时本地化和映射，地图构建，探测仪

Use simultaneous localization and mapping (SLAM) algorithms to build maps surrounding the ego vehicle based on visual or lidar data. Use visual-inertial odometry to estimate the pose (position and orientation) of a vehicle based on data from onboard sensors such as inertial measurement units (IMUs).

Functions

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Rotations

`rigid3d`	3-D刚性几何变换
`quaternion`	Create a quaternion array
`区`	Angular distance in radians
`rotateframe`	四元框架旋转
`rotatepoint`	Quaternion point rotation
`rotmat`	将四个转换为旋转矩阵
`rotvec`	Convert quaternion to rotation vector (radians)
`rotvecd`	将四个旋转载体转换为旋转载体（度）
`parts`	Extract quaternion parts
`euler`	将四个转换为欧拉角（弧度）
`eulerd`	Convert quaternion to Euler angles (degrees)
`compact`	将四个阵列转换为N-by-4 matrix

Visual SLAM

`imageviewset`	Manage data for structure-from-motion, visual odometry, and visual SLAM
`optimizePoses`	使用相对姿势约束优化绝对姿势
`createPoseGraph`	Create pose graph
`Relativecamerapose`	计算相对旋转和相机姿势之间的翻译
`三角剖分`	立体声图像中未发生匹配点的3-D位置
`bundleAdjustment`	Adjust collection of 3-D points and camera poses
`bundleAdjustmentMotion`	Adjust collection of 3-D points and camera poses using motion-only bundle adjustment
`bundleAdjustmentStructure`	使用仅结构捆绑调整来完善3-D点

点云猛击

`pcviewset`	Manage data for point cloud based visual odometry and SLAM
`optimizePoses`	使用相对姿势约束优化绝对姿势
`createPoseGraph`	Create pose graph
`scanContextDistance`	扫描上下文描述符之间的距离
`scanContextDescriptor`	Extract scan context descriptor from point cloud
`PCTRANSFORM`	变换3-D点云
`PCALIGN`	Align an array point clouds
`pcRegistercorr`	Register two point clouds using phase correlation
`PCRegisterCPD`	Register two point clouds using CPD algorithm
`pcregistericp`	Register two point clouds using ICP algorithm
`pcRegisterndt`	Register two point clouds using NDT algorithm
`pcregisterloam`	Register two point clouds using LOAM algorithm
`pcmapndt`	基于正常分布变换（NDT）的本地化图

Topics

Rotations, Orientations, and Quaternions for Automated Driving
Quaternions are four-part hypercomplex numbers that are used to describe three-dimensional rotations and orientations. Learn how to use them for automated driving applications.
在MATLAB中实施视觉大满贯
Understand the visual simultaneous localization and mapping (vSLAM) workflow and how to implement it using MATLAB.
Monocular Visual Simultaneous Localization and Mapping
Visual simultaneous localization and mapping (vSLAM).
Implement Point Cloud SLAM in MATLAB
Understand point cloud registration and mapping workflow.

Featured Examples

Lidar Localization with Unreal Engine Simulation

Develop and evaluate a lidar localization algorithm using synthetic lidar data from the Unreal Engine^®simulation environment.

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使用虚幻引擎开发视觉SLAM算法Simulation

Develop a visual simultaneous localization and mapping (SLAM) algorithm using image data from the Unreal Engine simulation environment.

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Build a Map from Lidar Data

Process 3-D lidar sensor data to progressively build a map, with assistance from inertial measurement unit (IMU) readings.

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Build a Map from Lidar Data Using SLAM

Process lidar data to build a map and estimate a vehicle trajectory using simultaneous localization and mapping.

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Build a Map with Lidar Odometry and Mapping (LOAM) Using Unreal Engine Simulation

通过使用UnrealEngine®仿真环境中的合成LIDAR数据，使用LIDAR的进程和映射（壤土）[1]算法构建地图[1]算法。在此示例中，您将学习如何：

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Build Occupancy Map from 3-D Lidar Data using SLAM

使用同时定位和映射（SLAM）算法从3-D LIDAR数据构建2D占用率图。

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Build Map and Localize Using Segment Matching

使用LIDAR数据构建地图，并使用SegMatch [1]在地图上定位车辆的位置，这是基于段匹配的位置识别算法。

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Visual-Inertial Odometry Using Synthetic Data

Estimate the position and orientation of a vehicle by using an inertial measurement unit (IMU) and a monocular camera.

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Visual Localization in a Parking Lot

Develop a visual localization system using synthetic image data from a parking lot scene in Unreal Engine environment.

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使用虚幻引擎模拟基于感知的停车位检测

Build a bird's-eye-view map of a parking lot using semantically segmented images from the ego vehicle camera, and detect empty parking spots from the map.

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