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| obstacle_detection [2016/03/10 13:51] – created zyuan | obstacle_detection [2026/10/22 23:03] (current) – external edit 127.0.0.1 | ||
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| - | We use the package | + | We use the **[[http:// |
| + | ====== Objectif ====== | ||
| + | |||
| + | We catched the depth data and tranformed to point cloud. Then we filter that object which is higher than the ground. We projected this result to create a 2D image. By using this image, we can send back reference position of an obstacle. | ||
| + | * You can also take a look to the tutorial: | ||
| + | |||
| + | Calibration Parameters for the calculation of 3D coordinates from the raw image measurements include: | ||
| + | **The height of Kinect.** | ||
| + | (In addition, we found that y-axis of the point cloud corresponds to the height, means the z-axis for a tf-tranform of Kinect Sensor) | ||
| + | |||
| + | ===== package ===== | ||
| + | |||
| + | The package: {{: | ||
| + | |||
| + | 1. Launch Kinect Sensor | ||
| + | * //roslaunch openni_launch openni.launch// | ||
| + | 2. Run | ||
| + | * //rosrun pcl_detector pcl_detector// | ||
| + | |||
| + | ==== Simulation ==== | ||
| + | |||
| + | You can simulate the result by using rviz: | ||
| + | * //rosrun rviz rviz// | ||
| + | |||
| + | Choose the output topic " | ||
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