Tuning down PointCloud2 data for smaller computation devices

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Hello,

I have installed ROS navigation on a Jetson TK 1 microprocessor and I am using Kinect for /scan sensory using laser_to_scan node.

I have added the scan topic inside costmap_common_params.yaml

observation_sources:  scan
scan:
data_type: LaserScan
topic: kinect_scan

It computes the AMCL and executes tway points pretty well but now I would like to add the point cloud topic as well so that it avoids obstacles in the way using the local costmap obstacle avoidance.

bump:
data_type: PointCloud2
topic: camera/depth/points

Unfortunately by doing this I have slowed down the path planning drastically.

Sending a goal pose from RVIZ makes the ros navigation about 25 seconds to send the first plan and even worse, since it doesnt send the next plan in time, the path planning fails altogether.

It seems that the CPU on the Tegra might not be managing all the computation required from the pointcloud topic so I was wondering if there were ways to tune the data down or even take only a chunk from it so that move_base can compute with less resource consumption.

Thanks in advance.