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--- obstacle_detection [2016/03/13 08:53] – zyuan
+++ obstacle_detection [2026/10/22 23:03] (current) – external edit 127.0.0.1
@@ Line 1: / Line 1: @@
-We use the **[[Point Cloud Library PCL]][[http://pointclouds.org/documentation/]]**  that aims at detecting obstacles (with Kinect).
+We use the **[[http://pointclouds.org/documentation/|Point Cloud Library PCL]]**  that aims at detecting obstacles (with Kinect).
+====== 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:[[http://wiki.ros.org/pcl/Tutorials]]
+  * You can also take a look to the tutorial:[[http://wiki.ros.org/pcl/Tutorials]]
 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)
+**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)
-You can simulate the result by using rviz:
+===== package =====
-$rosrun rviz rviz
-Choose the output topic "output" in type of "PointCloud2"
-TODO: add FindingBlob to optimize the results.
 The package: {{:pcl_detector.tar.gz|}}
-To use it:
 . Launch Kinect Sensor
-$roslaunch openni_launch openni.launch
+  * //roslaunch openni_launch openni.launch//
 . Run
-$rosrun pcl_detector pcl_detector
+  * //rosrun pcl_detector pcl_detector//
+==== Simulation ====
+You can simulate the result by using rviz:
+  * //rosrun rviz rviz//
+Choose the output topic "output" in type of "PointCloud2"