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).
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 multi-sensor SLAM solutions using robust pose graph optimization. Use interactive tools to review and modify loop closures.
Path Planning
Find paths through diverse environments using customizable sampling-based planners such as RRT and RRT*, or search-based planners such as A* and Hybrid A*.
Sensor Modeling
Model and tune parameters for various sensors such as IMU, GPS, GNSS, wheel encoders, and range finders. Visualize sensor orientation, velocity, trajectories, and measurements.
Multisensor Pose Estimation
Localize ground and aerial vehicles using inertial sensors with or without GPS. Automatically tune filters to minimize pose estimation error.
Navigation in Dynamic Environments
Plan local trajectories around a global path while avoiding moving obstacles. Follow the planned path or trajectories using Control algorithm.
Use Design Patterns to Boost Performance
Design patterns, including stencil processing and reductions, are applied automatically when available to increase the performance of generated code. You can also manually invoke them using specific pragmas.
Log Signals, Tune Parameters, and Verify Code Behavior
Use GPU Coder with Simulink Coder to log signals and tune parameters in real time. Add Embedded Coder to interactively trace between MATLAB and generated CUDA code to numerically verify the behavior of generated CUDA code via SIL testing.
Accelerate MATLAB and Simulink Simulations
Call generated CUDA code as a MEX function from your MATLAB code to speed execution. Use Simulink Coder with GPU Coder to accelerate compute-intensive portions of MATLAB Function blocks in your Simulink models on NVIDIA GPUs.
