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).
This example reviews concepts in three-dimensional rotations and how quaternions are used to describe orientation and rotations.
This example shows how to simulate inertial measurement unit (IMU) measurements using the
imuSensor (Sensor Fusion and Tracking Toolbox) System object.
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.
This example demonstrates how to match two laser scans using the Normal Distributions Transform (NDT) algorithm .
This example shows how to use the rapidly-exploring random tree (RRT) algorithm to plan a path for a vehicle through a known map.
This example demonstrates how to implement the Simultaneous Localization And Mapping (SLAM) algorithm on a collected series of lidar scans using pose graph optimization.
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.
Navigation Toolbox Overview
Learn about the various functionality supported in Navigation Toolbox