ROS Basics – depthimage_to_laserscan with low cost depth sensors Asus Xtion or Microsoft Kinect

Most of my work depended on the efficient connection between the [amazon &title=Asus Xtion&text=Asus Xtion] and the [amazon &title=CubieTruck&text=CubieTruck] as a low cost laser scanner. As the [amazon &title=Asus Xtion&text=Asus Xtion] usually delivers 3D  sensor_msgs/PointCloud  data and most slamming algorithms need 2D  sensor_msgs/LaserScan messages to work properly, we need to find a solution to this issue: depthimage_to_laserscan .

If you already managed to use the ros-indigo-openni2-camera and  ros-indigo-openni2-launch you can use the following code:

As you might see, the  depthimage_to_laserscan gets initialized in a separate nodelet manager.

Nodelets are designed to provide a way to run multiple algorithms on a single machine, in a single process, without incurring copy costs when passing messages intraprocess. (Quote Wiki)

They hugely improve the performance of our 3D point clouds and allow significantly higher publishing rates.

An important property of an robot is the rate of data creation. A low rate influences most
higher algorithms leads to incorrect results. In most cases especially the depth sensors are
required to publish sufficient material to create detailed maps. The [amazon &title=Asus Xtion&text=Asus Xtion] Pro driver OpenNi2 and the ROS package openni2_camera offers multiple run modes which can be set by dynamic_reconfigure (I suggest using it in combination with rqt). Another essential option influencing performance is the data_skip parameter, which allows the system to skip a certain amount of pictures the hardware produces before loading them into memory and by that remarkably reduces computational load. It can be set to an integer value between zero, which means not to skip any frames at all, and ten, leading to every eleventh frame to be processed.

Performance Check

The different combinations of resolutions, maximum frequencies and the data_skip -parameter
ran on the aMoSeRo (my low cost [amazon &title=CubieTruck&text=CubieTruck] robot) is illustrated in the table below.  As it can be seen, especially the amount of frames that has to be processed per second highly influences the complete system.

 

In conlusion, the depthimage_to_laserscan package is really useful when working with low cost setups like depth sensors [amazon &title=Asus Xtion&text=Asus Xtion] or the [amazon &title=Kinect&text=Microsoft Kinect]. It furthermore is essential when interfacing SLAM algorithms.