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Navigation Toolbox
Design, simulate, and deploy alogrithms for planning and navigation
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™).
Mapping and Localization
Create an occupancy map of the environment using SLAM algorithms. Use pose estimation to localize a vehicle.
Simultaneous Localization and Mapping (SLAM)
Implement SLAM algorithms with lidar scans using pose graph optimization. Use SLAM Map Builder app to find and modify loop closures. Build and export the resulting map as an occupancy grid.
Implement Simultaneous Localization and Mapping (SLAM) with Lidar Scans
Map generation using lidar SLAM
Localization and Pose Estimation
Apply Monte Carlo Localization (MCL) to estimate the position and orientation of a vehicle using sensor data and a map of the environment.
Estimate pose of nonholonomic and aerial vehicles using inertial sensors and GPS. Determine pose without GPS by fusing inertial sensors with altimeters or visual odometry.
IMU and GPS Fusion for Inertial Navigation
Monte Carlo Localization in an indoor environment.
2D and 3D Map Representations
Create a binary or probabilistic occupancy grid using real or simulated sensor readings. Use egocentric maps that are fast to query and memory efficient.
Create Egocentric Occupancy Maps Using Range Sensors
Build Occupancy Map from Depth Images Using Visual Odometry and Optimized Pose Graph
3D occupancy grid visualization.
Motion Planning
Use extensible path planners, choose optimal paths, and compute steering commands for path following.
Path Planning
Use sampling-based path planners such as Rapidly-Exploring Random Tree (RRT) and RRT* to find a path from start to goal locations. Adapt the planner interface to your application’s state space. Use Dubins and Reeds-Shepp motion primitives to create smooth, drivable paths.
Plan Mobile-Robot Paths Using RRT
Motion Planning with RRT for a Robot Manipulator
Metrics for Path Planning
Use metrics to validate paths for smoothness and clearance from obstacles. Choose the best path using numerical and visual comparisons.
Path Following and Controls
Tune control algorithms to follow a planned path. Compute steering and velocity commands using vehicle motion models. Avoid obstacles with algorithms such as vector field histogram.
Sensor Modeling and Simulation
Simulate measurements from IMUs, GPS receivers, and range sensors under various environmental conditions.
Sensor Models
Model IMU, GPS, and INS sensors. Tune parameters such as temperature and noise to emulate real-world conditions. Estimate distances to objects using range sensors and measure vehicle motion with odometry sensors.
Sensor Motion Simulation
Plot a vehicle’s orientation, velocity, trajectories, and sensor measurements. Generate trajectories to emulate sensors traveling through the world. Export trajectories to external simulators or to a scenario designer.
