Optimized ROS->PCL conversion

facontidavide · 2022-05-31T09:16:19.646Z

Hi perception engineers!

if you use PCL with ROS, you are certainly using the function pcl::fromROSMsg to converts sensor_msgs::PointCloud2 to pcl::PointCloud<PointT>.

This function consumes a considerable amount of CPU and we haven’t started processing the PointCloud yet!

Luckily, you can make this function almost 2 times faster.

The implementation is available here: Drop in replacement for pcl::fromROSMsg · GitHub

You can easily convert this to ROS2, simply renaming the namespace of the sensors_msgs.

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A PR has already been submitted, but if you want to take advantage of this improvement right now, you may try the stand-alone file I shared.

Cheers

PS: I also take the opportunity that friends don’t let friends allocate memory. Remember that you should try to reuse existing pointclouds in each loop, instead of allocating new ones.

peci1 · 2022-05-31T10:42:03.971Z

Great discovery, Davide! Could you elaborate shortly on what does the improvement lie in?

I’ll also add a link to a relevant piece of information - pcl::toPclPointCloud2 and pcl::toROSMsg keep the alignment bytes introduced by the PCL library to make memory operations faster. But inflating the message size might decrease preformance elsewhere…

facontidavide · 2022-05-31T11:20:29.120Z

Very easy! The new function is as efficient as pcl::moveFromROS would be, if I could use it.
Unfortunately, we can not move the sensor_msg, because it is a ConstReference.

This obliges the original implementation to copy data from sensor_msgs::PointCloud2 to pcl::PointCloud2.

I skip that part, modifying the pcl::PointCloud2 to pclPintCloud<T> function, to make it able to accept the data from sensor_msgs.

Apart from that, I am not doing anything that PCL isn’t doing.

system · 2022-06-30T11:21:19.227Z

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