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--- software:uima:setup:rhdemo [2014/05/14 15:22] – [Information and troubleshooting with the robohow demo] balintbe
+++ software:uima:setup:rhdemo [2016/05/19 09:19] (current) – external edit 127.0.0.1
@@ Line 1: / Line 1: @@
-====== Information and troubleshooting with the robohow demo ======
+====== Running RoboSherlock for the robohow demo ======
-__Important:__ Before starting anything, make sure that the Kinect on the robot is set to high resolution mode, and the robot is localized. High resolution is important to get correct poses for objects that are detected based on RGB images. Filtering the pointclouds/depth-image in order to get data only from the regions of interest (the two counter tops and the table), requires the robot to be localized. Slight errors in localization are OK, but false detections might happen.
+__Important:__ Before starting anything, make sure that the Kinect on the robot is set to high resolution mode, and the robot is localized. High resolution is important to get correct poses for objects that are detected based on RGB images.
+To do this:
+  rosrun rqt_reconfigure rqt_reconfigure
+Find //kinect_head//, select //driver// and change //image_mode// from **VGA_30HZ** to **SXGA_15Hz**.
+Filtering the pointclouds/depth-image in order to get data only from the regions of interest (the two counter tops and the table), requires the robot to be localized. Slight errors in localization are OK, but larger errors lead to false oject detections.
 ==Running the perception pipeline for the demo==
@@ Line 23: / Line 29: @@
     </nameValuePair>
 Uncomment the line conatining urdf filter and comment the line after it.
 __Note2__:Robot self filtering does not work on the demo PC yet.
+==Using MLNs==
 To use the results from the MLN based inferencing run:
   roslaunch mln_query mln_query.launch
+The mln atoms generator and the inferencer are by default in the pipeline, but the demo will run even we are not running it.
 ==Pipeline definition==
@@ Line 55: / Line 65: @@
   </flowConstraints>
-To turn on or off modules simply (un)comment them. No recompilation needed. All of the modules in the pipeline have a configuration file of their own, located in //{PROJ_SOURCE}/descriptors/annotators//. Parameters of the algorithms can be changed here (e.g. min cluster size, min. plane size etc.)
+To turn on or off modules simply (un)comment them. No recompilation needed. All of the modules in the pipeline have their own configuration file, located in //{PROJ_SOURCE}/descriptors/annotators//. Parameters of the algorithms can be changed here (e.g. min cluster size, min. plane size, binary threshold limit etc.)
 ===== Troubelshooting =====
+You can use the Visual output of RoboSherlock to see what the perception system thinks it sees. Select the window and press //left// and //right// to navigate through the results of different annotators
+For debugging purposes, there is a module called TFBroadcaster, which will publish to tf the poses of the clusters that it sees. It is **not** recommended to run this modul during the actual demo!
+Most common problem when perceiving an object in the wring pose is that the robot moved, but perception was still processing an old frame. Take care when you start listening for results! In it's current state the whole pipeline might take up to **5 seconds**!!! (sorry bout that:D)
+If spatulas are not seen try fiddleing with the lights :D