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Ground Vehicle Algorithms

Map utilization, path planning, path following, state estimation

These Robotics System Toolbox™ algorithms focus on mobile robotics applications (i.e. ground vehicles). These classes help you with the whole mobile robotics workflow. You can create maps of environments using occupancy grids, develop path planning for robots in a given environment, and tune controllers to follow a set of waypoints. Also, you can perform obstacle avoidance, state estimation, and localization based on sensor data from your robot.


readBinaryOccupancyGridRead binary occupancy grid
writeBinaryOccupancyGridWrite values from grid to ROS message
lidarScanCreat object for storing 2-D lidar scan
matchScansEstimate pose between two laser scans
plotDisplay laser or lidar scan readings
removeInvalidDataRemove invalid range and angle data
transformScanTransform laser scan based on relative pose


robotics.BinaryOccupancyGridCreate occupancy grid with binary values
robotics.OccupancyGridCreate occupancy grid with probabilistic values
robotics.PRMCreate probabilistic roadmap path planner
robotics.PurePursuitCreate controller to follow set of waypoints
robotics.VectorFieldHistogramAvoid obstacles using vector field histogram
robotics.MonteCarloLocalizationLocalize robot using range sensor data and map
robotics.OdometryMotionModelCreate an odometry motion model
robotics.ParticleFilterCreate particle filter state estimator


Pure PursuitLinear and angular velocity control commands
Vector Field HistogramAvoid obstacles using vector field histogram


Mapping and Path Planning

Occupancy Grids

Details of occupancy grid functionality and map structure.

Probabilistic Roadmaps (PRM)

How the PRM algorithm works and specific tuning parameters.

Path Planning in Environments of Different Complexity

This example demonstrates how to compute an obstacle free path between two locations on a given map using the Probabilistic Roadmap (PRM) path planner.

Mapping With Known Poses

This example shows how to create a map of the environment using range sensor readings if the position of the robot is known at the time of sensor reading.

Compose a Series of Laser Scans with Pose Changes

Use scan matching to compose a series of laser scans

Robot Control

Pure Pursuit Controller

Pure Pursuit Controller functionality and algorithm details.

Path Following for a Differential Drive Robot

This example demonstrates how to control a robot to follow a desired path using a Robot Simulator.

Vector Field Histogram

VFH algorithm details and tunable properties.

Obstacle Avoidance with TurtleBot and VFH

This example shows how to use a TurtleBot® with Vector Field Histograms (VFH) to perform obstacle avoidance when driving a robot in an environment.

State Estimation

Particle Filter Parameters

A particle filter is a recursive, Bayesian state estimator that uses discrete particles to approximate the posterior distribution of the estimated state.

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.

Track a Car-Like Robot Using Particle Filter

Particle filter is a sampling-based recursive Bayesian estimation algorithm.

Monte Carlo Localization Algorithm

The Monte Carlo Localization (MCL) algorithm is used to estimate the position and orientation of a robot in its environment using a known map of the environment, range sensor data, and odometry sensor data.

Localize TurtleBot Using Monte Carlo Localization

This example demonstrates an application of the Monte Carlo Localization (MCL) algorithm on TurtleBot® in simulated Gazebo® environment.

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