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The navigation stack will support your use case out of the box -- because you have a true long-range laser for localization (which that other problem you reference did not). You might note that this is nearly identical forms of input as the PR2 uses, which has a Hokuyo laser on the base for localization and obstacle avoidance, and a stereo camera rig for additional obstacle avoidance in 3d.

When configuring your robot's launch and parameter files, use only the SICK laser as input to AMCL for localization. Then use both the SICK and the Kinect data as observation sources for the local costmap (see http://www.ros.org/wiki/costmap_2d for details on parameters).

It might also be advisable to setup a voxel_grid to downsample and clean your Kinect data before sending it into the costmap_2d. The pcl_ros contains a nodelet that can do such, so you could configure it entirely in a launch file without any new custom code (see http://www.ros.org/wiki/pcl_ros/Tutorials/VoxelGrid%20filtering for details)

The navigation stack will support most of your use case out of the box -- because you have a true long-range laser for localization (which that other problem you reference did not). You might note that this is nearly identical forms of input as the PR2 uses, which has a Hokuyo laser on the base for localization and obstacle avoidance, and a stereo camera rig for additional obstacle avoidance in 3d.

When configuring your robot's launch and parameter files, use only the SICK laser as input to AMCL for localization. Then use both the SICK and the Kinect data as observation sources for the local costmap (see http://www.ros.org/wiki/costmap_2d for details on parameters).

It might also be advisable to setup a voxel_grid to downsample and clean your Kinect data before sending it into the costmap_2d. The pcl_ros contains a nodelet that can do such, so you could configure it entirely in a launch file without any new custom code (see http://www.ros.org/wiki/pcl_ros/Tutorials/VoxelGrid%20filtering for details)