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Navigation Toolbox 入門


Navigation Toolbox™ は、運動の計画およびナビゲーションを行うシステムを設計するためのアルゴリズムと解析ツールを提供します。ツールボックスには、カスタマイズ可能な検索とサンプリングベースのパスプランナーが含まれています。また、マルチセンサー姿勢推定用のセンサー モデルとアルゴリズムも含まれています。独自のデータを使用して 2 次元および 3 次元のマップ表現を作成したり、ツールボックスに含まれている SLAM (位置推定とマッピングの同時実行) アルゴリズムを使用してマップを生成したりできます。自動運転およびロボット工学のアプリケーション向けの参考例が提供されています。

パスの最適性、滑らかさおよびパフォーマンスのベンチマークを比較するためのメトリクスを生成できます。SLAM マップ ビルダー アプリにより、マップの生成を対話的に可視化してデバッグすることができます。アルゴリズムをハードウェアに直接展開して (MATLAB® Coder™ または Simulink® Coder を使用)、アルゴリズムをテストできます。


  • Rotations, Orientation, and Quaternions

    This example reviews concepts in three-dimensional rotations and how quaternions are used to describe orientation and rotations. Quaternions are a skew field of hypercomplex numbers. They have found applications in aerospace, computer graphics, and virtual reality. In MATLAB®, quaternion mathematics can be represented by manipulating the quaternion class.

  • Introduction to Simulating IMU Measurements

    This example shows how to simulate inertial measurement unit (IMU) measurements using the imuSensor (Sensor Fusion and Tracking Toolbox) System object. An IMU can include a combination of individual sensors, including a gyroscope, an accelerometer, and a magnetometer. You can specify properties of the individual sensors using gyroparams (Sensor Fusion and Tracking Toolbox), accelparams (Sensor Fusion and Tracking Toolbox), and magparams (Sensor Fusion and Tracking Toolbox), respectively.

  • 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 [1]. The goal of scan matching is to find the relative pose (or transform) between the two robot positions where the scans were taken. The scans can be aligned based on the shapes of their overlapping features.

  • 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. Special vehicle constraints are also applied with a custom state space. You can tune your own planner with custom state space and path validation objects for any navigation application.

  • 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. The goal of this example is to build a map of the environment using the lidar scans and retrieve the trajectory of the robot.

  • 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. The goal of this example is to estimate the trajectory of the robot and create a 3-D occupancy map of the environment from the 3-D lidar point clouds and estimated trajectory.


Navigation Toolbox の概要
Navigation Toolbox でサポートされている各種の機能について学ぶ