Localization

Simultaneous localization and mapping, map building, odometry

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 rigid geometric transformation
`quaternion`	Create a quaternion array
`dist`	Angular distance in radians
`rotateframe`	Quaternion frame rotation
`rotatepoint`	Quaternion point rotation
`rotmat`	Convert quaternion to rotation matrix
`rotvec`	Convert quaternion to rotation vector (radians)
`rotvecd`	Convert quaternion to rotation vector (degrees)
`parts`	Extract quaternion parts
`euler`	Convert quaternion to Euler angles (radians)
`eulerd`	Convert quaternion to Euler angles (degrees)
`compact`	Convert quaternion array to N-by-4 matrix

Visual SLAM

`imageviewset`	Manage data for structure-from-motion, visual odometry, and visual SLAM
`optimizePoses`	Optimize absolute poses using relative pose constraints
`createPoseGraph`	Create pose graph
`relativeCameraPose`	Compute relative rotation and translation between camera poses
`triangulate`	3-D locations of undistorted matching points in stereo images
`bundleAdjustment`	Refine camera poses and 3-D points
`bundleAdjustmentMotion`	Refine camera pose using motion-only bundle adjustment
`bundleAdjustmentStructure`	Refine 3-D points using structure-only bundle adjustment

Lidar SLAM

`pcviewset`	Manage data for point cloud based visual odometry and SLAM
`optimizePoses`	Optimize absolute poses using relative pose constraints
`createPoseGraph`	Create pose graph
`scanContextDistance`	Distance between scan context descriptors
`scanContextDescriptor`	Extract scan context descriptor from point cloud
`pctransform`	Transform 3-D point cloud
`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

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.

Monocular Visual Simultaneous Localization and Mapping (Computer Vision Toolbox)

Visual simultaneous localization and mapping (vSLAM).