Get Started with Navigation Toolbox
Navigation Toolbox™ provides algorithms and analysis tools for designing motion planning and navigation systems. The toolbox contains customizable search and sampling-based path-planners. It also contains sensor models and algorithms for multi-sensor pose estimation. You can create 2D and 3D map representations using your own data or generate maps using the simultaneous localization and mapping (SLAM) algorithms included in the toolbox. Reference examples are provided for self-driving and robotics applications.
You can generate metrics for comparing path optimality, smoothness, and performance benchmarks. The SLAM map builder app lets you interactively visualize and debug map generation. You can test your algorithms by deploying them directly to hardware (with MATLAB® Coder™ or Simulink® Coder).
Tutorials
- Rotations, Orientation, and Quaternions
This example reviews concepts in three-dimensional rotations and how quaternions are used to describe orientation and rotations.
- Introduction to Simulating IMU Measurements
This example shows how to simulate inertial measurement unit (IMU) measurements using the
imuSensor(Sensor Fusion and Tracking Toolbox) System object. - Estimate Position and Orientation of a Ground Vehicle
This example shows how to estimate the position and orientation of ground vehicles by fusing data from an inertial measurement unit (IMU) and a global positioning system (GPS) receiver.
- Estimate Robot Pose with Scan Matching
This example demonstrates how to match two laser scans using the Normal Distributions Transform (NDT) algorithm [1].
- Plan Mobile Robot Paths using RRT
This example shows how to use the rapidly-exploring random tree (RRT) algorithm to plan a path for a vehicle through a known map.
- Implement Simultaneous Localization And Mapping (SLAM) with Lidar Scans
This example demonstrates how to implement the Simultaneous Localization And Mapping (SLAM) algorithm on a collected series of lidar scans using pose graph optimization.
- Perform SLAM Using 3-D Lidar Point Clouds
This example demonstrates how to implement the Simultaneous Localization And Mapping (SLAM) algorithm on collected 3-D lidar sensor data using point cloud processing algorithms and pose graph optimization.
Videos
Navigation Toolbox Overview
Learn about the various functionality supported in Navigation Toolbox