Navigation Toolbox
Design, simulate, and deploy algorithms for autonomous navigation
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Navigation Toolbox provides algorithms and analysis tools for sensor modeling and calibration, motion planning, simultaneous localization and mapping (SLAM), and inertial navigation. 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 multisensor pose estimation.
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.
Reference examples are provided for aircraft, 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).
IMU Sensor
Model and tune parameters for IMU sensors, including accelerometer, gyroscope, and magnetometer characteristics. Configure noise profiles, biases, and drift, and perform calibration to match real-world conditions. Visualize orientation, velocity, trajectories, and raw or fused measurements.
Multisensor Pose Estimation
Localize vehicles using inertial sensors with or without GPS. Automatically tune filters to minimize pose estimation error.
GPS/GNSS Analysis
Integrate GPS/GNSS sensor models into simulations. Import satellite navigation and observation data and analyze parameters such as satellite visibility and noise.
Map Representation
Create 2D and 3D occupancy grids. Use multilayer maps to store generic data such as costs. Represent obstacles using capsule-based collision objects.
Simultaneous Localization and Mapping (SLAM)
Implement customized multisensor SLAM solutions using pose graph and factor graph optimization. Customize SLAM with interactive tools and deploy ROS nodes.
Motion Planning
Find paths through diverse 2D and 3D environments using customizable sampling-based planners such as RRT and RRT*, or search-based planners such as A* and Hybrid A*.
Aerospace
Simulate and deploy robust inertial navigation systems to operate with or without GNSS/GPS positioning. Tune and deploy inertial sensor fusion and evaluate the effects of sensor parameters and fusion filters.
Automotive
Plan and re-plan vehicle motion based on environment using dynamic maps, reference paths and vehicle aware motion planners. Fuse map information from multiple sensors.
Product Resources:
“Using MATLAB and Simulink, we designed a prototype for the motion controller and tested it on the hardware within a month. The localization algorithm was evaluated and challenges were clarified by performing simulations.”
