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This package provides object pose prediction on the basis of the alternative scene model PSM (Probabilistic Scene Model): It generates hypotheses (type and pose) for missing objects in the context of 3D object search. Moreover, it integrates scene recognition and object pose prediction (both on the basis of PSM) into Active Scene Recognition. Its output are asr_next_best_view/AttributedPointCloud.msg messages which can be processed by asr_next_best_view.

There are two launch files. The first launches the PSM pose prediction (launch/psm_node.launch). The second starts the PSM pose prediction with given likelihoods (launch/recognizer_prediction_psm.launch).


Builds a scene graph of each given scene and occupies nodes (objects) with the given unobserved objects (evidences). Based on the absolute positions of the observed objects the relative poses are calculated by sampling the Gaussian Mixture Models of the child nodes. The number of hypotheses is spread equally to each unobserved object. If there is more than one observed object, the first object in the object list that is part of the scene will be treated as reference objects. The reference object is the root node of the scene graph.

Output: asr_next_best_view/AttributedPointCloud pose_hypothesis (see topics) and bool all_scene_objects_found: true if all scene objects of ALL scenes in the .xml have been found. The automat can then go to success state, false otherwise.


Needed packages

Needed software

Start system

with inference:

start roscore and the kinematic chain:

roslaunch asr_kinematic_chain_dome transformation_publishers_for_kinect_left.launch

start the server: roslaunch asr_recognizer_prediction_psm psm_node.launch

without inference:

start roscore and the kinematic chain (see above).

start the server: roslaunch asr_recognizer_prediction_psm recognizer_prediction_psm.launch

Visualization: Start RViz (rviz) and open the config file doc/recognizer_prediction_psm. rviz


Only differs from real application in source of observed object messages.

ROS Nodes

Published Topics


with inference: (psm_node.launch)

without inference:

The number of the scene_probabilities has to be equal to the number of scenes.

The sum of the scene_probabilites should be 1.0, otherwise the number of hypotheses will not be correct.

There has to be at least one observed object.

further parameters:

In the recognizer_prediction_psm.launch file you can set the lifetime of the rviz markers. You can also enable additional console output for debugging.

AsrPSM parameters (compare there) are stored in param/properties.yaml

Needed Services

asr_msgs::!AsrObject[] objects: input. The list of all observed objects (with inference)

Provided Services



Returns an asr_next_best_view::!AttributedPointCloud message named pose_hypothesis.

This represents the generated hypotheses for all unobserved objects that are part of the scenes.

Each point in the cloud (AsrAttributedPoint) consists of a string type and a predicted geometry_msgs::Pose pose

of the object.

The point cloud of the poses can be processed by the AsrNextBestView package.

Also returns whether all scene objects have been found (true or false).

psm_node_server, which offers the /psm_node service, uses the /recognizer_prediction_psm service offered by recognizer_prediction_psm internally (through the asr_recognizer_prediction_psm_client).



2024-06-15 12:30