위치추정과 자세 추정

이 예제는 UAV(무인 항공기) 또는 쿼드콥터에 적합한 IMU + GPS 융합 알고리즘을 작성하는 방법을 보여줍니다.

Estimate the pose (position and orientation) of a ground vehicle using an inertial measurement unit (IMU) and a monocular camera. In this example, you:

MATLAB Mobile™ reports sensor data from the accelerometer, gyroscope, and magnetometer on Apple or Android mobile devices. Raw data from each sensor or fused orientation data can be obtained. This example shows how to compare the fused orientation data from the phone with the orientation estimate from the ahrsfilter object.

이 예제는 IMU(관성 측정 장치)와 GPS(위성 측위 시스템) 수신기에서 제공되는 데이터를 융합하여 지상 이동체의 위치와 방향을 추정하는 방법을 보여줍니다.

Combine robot odometry data and AprilTag markers to implement SLAM with a factor graph and pose graph, and compare the results.

Reduce the drift in the estimated trajectory (location and orientation) of a monocular camera using 3-D pose graph optimization. Visual odometry estimates the current global pose of the camera (current frame). Because of poor matching or errors in 3-D point triangulation, robot trajectories often tends to drift from the ground truth. Loop closure detection and pose graph optimization reduce this drift and correct for errors.

Implement monocular visual-inertial odometry to localize a UAV using camera and IMU data, optimized by a factor graph.