Navigation and Mapping

Point cloud registration and map building, 2-D and 3-D SLAM, and 2-D obstacle detection

A key component for advanced driver assistance systems (ADAS) applications and autonomous robots is enabling awareness of where the vehicle or robot is, with respect to its surroundings and using this information to estimate the best path to its destination. The simultaneous localization and mapping (SLAM) process uses algorithms to estimate the pose of a vehicle and the map of the environment at the same time.

Lidar Toolbox™ provides a point cloud registration workflow that uses the fast point feature histogram (FPFH) algorithm to stitch together point cloud sequences. You can use this feature for progressive map building. Such a map can facilitate path planning for vehicle navigation or can be used for SLAM. For an example of how to use the extractFPFHFeatures function in a 3-D SLAM workflow for aerial data, see Aerial Lidar SLAM Using FPFH Descriptors.

Lidar Toolbox also provides features for scan matching and simulating range-bearing sensor readings. These features are used in 2-D SLAM and obstacle detection workflows

Functions

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Pose Estimation

`matchScans`	Estimate pose between two laser scans
`matchScansGrid`	Estimate pose between two lidar scans using grid-based search
`matchScansLine`	Estimate pose between two laser scans using line features
`transformScan`	Transform laser scan based on relative pose

Sensor Model

`rangeSensor`	Simulate range-bearing sensor readings
`lidarSensor`	Simulate lidar sensor

Data Structures

`lidarScan`	Create object for storing 2-D lidar scan
`eigenFeature`	Object for storing eigenvalue-based features
`LOAMPoints`	Object for storing LOAM feature points

Registration

`pcregistericp`	Register two point clouds using ICP algorithm
`pcregistercpd`	Register two point clouds using CPD algorithm
`pcregistercorr`	Register two point clouds using phase correlation
`pcregisterndt`	Register two point clouds using NDT algorithm
`pcregisterloam`	Register two point clouds using LOAM algorithm
`detectLOAMFeatures`	Detect LOAM feature points from 3-D lidar data
`extractEigenFeatures`	Extract eigenvalue-based features from point cloud segments
`extractFPFHFeatures`	Extract fast point feature histogram (FPFH) descriptors from point cloud
`pcmatchfeatures`	Find matching features between point clouds
`pcmapsegmatch`	Map of segments and features for localization and loop closure detection
`pcshowMatchedFeatures`	Display point clouds with matched feature points

Topics

Implement Point Cloud SLAM in MATLAB
Understand point cloud registration and mapping workflow.
Estimate Transformation Between Two Point Clouds Using Features
This example shows how to estimate a rigid transformation between two point clouds.
Match and Visualize Corresponding Features in Point Clouds
This example shows how to match corresponding features between point clouds using the pcmatchfeatures function and visualize them using the pcshowMatchedFeatures function.

Featured Examples

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

Build a map with the lidar odometry and mapping (LOAM) [1] algorithm by using synthetic lidar data from the Unreal Engine® simulation environment. In this example, you learn how to:

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Build Map and Localize Using Segment Matching

Build a map with lidar data and localize the position of a vehicle on the map using SegMatch [1], a place recognition algorithm based on segment matching.

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Feature-Based Map Building from Lidar Data

Demonstrates how to process 3-D lidar data from a sensor mounted on a vehicle to progressively build a map. Such a map is suitable for automated driving workflows such as localization and navigation. These maps can be used to localize a vehicle within a few centimeters.

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Aerial Lidar SLAM Using FPFH Descriptors

Demonstrates how to implement the simultaneous localization and mapping (SLAM) algorithm for aerial mapping using 3-D features. The goal of this example is to estimate the trajectory of a robot and create a point cloud map of its environment.

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Build Map from 2-D Lidar Scans Using SLAM

Implement the simultaneous localization and mapping (SLAM) algorithm on a series of 2-D lidar scans using scan processing algorithms and pose graph optimization (PGO). The goal of this example is to estimate the trajectory of the robot and build a map of the environment.

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Collision Warning Using 2-D Lidar

Detect obstacles and warn of possible collisions using 2-D lidar data.

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