Get Started with Navigation Toolbox
Navigation Toolbox™ provides algorithms and analysis tools for motion planning, simultaneous localization and mapping (SLAM), and inertial navigation. The toolbox includes customizable search and sampling-based path-planners, as well as metrics for validating and comparing paths. You can create 2D and 3D map representations, generate maps using SLAM algorithms, and interactively visualize and debug map generation with the SLAM map builder app. The toolbox provides sensor models and algorithms for localization. You can simulate and visualize IMU, GPS, and wheel encoder sensor data, and tune fusion filters for multi-sensor pose estimation.
Reference examples are provided for automated driving, robotics, and consumer electronics applications. You can test your navigation algorithms by deploying them directly to hardware (with MATLAB® Coder™ or Simulink® Coder).
- Rotations, Orientation, and Quaternions
This example reviews concepts in three-dimensional rotations and how quaternions are used to describe orientation and rotations.
- Orientation, Position, and Coordinate Convention
Learn about toolbox conventions for spatial representation and coordinate systems.
- Introduction to Simulating IMU Measurements
This example shows how to simulate inertial measurement unit (IMU) measurements using the
- 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 .
- 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.
Navigation Toolbox Overview
Learn about the various functionalities supported in Navigation Toolbox