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https://github.com/bayesian-object-tracking/dbrt

Depth-Based Bayesian Robot Tracking
https://github.com/bayesian-object-tracking/dbrt

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Depth-Based Bayesian Robot Tracking

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README 

        
# ROS Depth-Based Robot Tracking Package (dbrt)



This package extends the object tracking packages dbot and dbot_ros to track 

articulated rigid bodies with several degree of freedom. In addition to depth

images, the robot tracker incorporates joint angle measurements at a higher 

rate, typically 100Hz-1kHz. Here are some of the core features



 * Provides joint state estimates at the rate of joint encoders

 * Compensates for inaccurate kinematics by estimating biases on the joint 

   angles

 * Estimates the head camera to robot base, if needed. Typically, the exact 

   camera location is unknown

 * Handles occlusion

 * Copes with camera delays 

 * Requires only the model, i.e. the URDF description including the link meshes.

 

For more details on the algorithm, please check https://am.is.tuebingen.mpg.de/publications/garciacifuentes-ral.



## Getting Started Example



First of all, set up and run the example, as described in the [Getting Started](https://github.com/bayesian-object-tracking/getting_started#robot-tracking)

documentation.



## Setting Up Your Own Robot



Now you can use the working example as a starting point. To use your own robot, you will need

its URDF, and you will need to modify some launch and config files in [dbrt_example](https://git-amd.tuebingen.mpg.de/open-source/dbrt_getting_started/tree/master/dbrt_example). The launch files

should be self explanatory and easy to adapt. You will need to edit 

the file fusion_tracker_gpu.launch (fusion_tracker_cpu.launch) to use

your own robot model, instead of Apollo. 



The main work will be to adapt the fusion_tracker_gpu.yaml 

(fusion_tracker_cpu.yaml) file to your robot. All the parameters 

for the tracking algorithm are specified in this file, and it is robot

specific. You will have to adapt the link and joint names to your robot.

Furthermore, you can specify which joints should be corrected using the 

depth images, how aggressively they should be corrected, and whether

you want to estimate an offset between the true camera and the 

nominal camera in your robot model. 



### URDF Camera Frame



Our algorithm assumes that the frame of the depth image (specified by

the camera_info topic) exists in your URDF robot model. You can check the camera frame

by running:

```bash

rostopic echo /camera/depth/camera_info

```

If this frame does not exist in your robot URDF, you have to add such a camera frame to the 

part of the robot where the camera is mounted. This requires 

connecting a camera link through a joint to another link of the robot. Take a 

look at [head.urdf.xacro](https://git-amd.tuebingen.mpg.de/open-source/dbrt_getting_started/blob/master/apollo_robot_model/models/head.urdf.xacro#L319) .



The XTION camera link *XTION_RGB* is connected to the link *B_HEAD* through the 

joint *XTION_JOINT*. The transformation between the camera and the robot is not 

required to be very precise, since our algorithm can estimate an offset. 

However, it must be accurate enough to provide 

a rough initial pose.



## How to cite?

```

@article{GarciaCifuentes.RAL,

 title = {Probabilistic Articulated Real-Time Tracking for Robot Manipulation},

 author = {Garcia Cifuentes, Cristina and Issac, Jan and W{\"u}thrich, Manuel and Schaal, Stefan and Bohg, Jeannette},

 journal = {IEEE Robotics and Automation Letters (RA-L)},

 volume = {2},

 number = {2},

 pages = {577-584},

 month = apr,

 year = {2017},

 month_numeric = {4}

}

```