[SOLVED] SLAM with kinect2

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I tested Kinect V2 with Kinect Fusion and RBDSLAM. For KinFu it was necessary to implement a lens distortion model in KinFu because the new Kinect causes much more distortion than the old Kinect.

Overall the results with the new Kinect are worse than with the old Kinect due to much noise and reflections (See my post in the PCL Mailing List)

At he bottom you can find the rgbdslam launch file which I used with kinect2_bridge. This file should be for a recorded datasets, you can open a bag file with the rgbdslam GUI. But I used live data, too. Unfortunately, I do note remember if this launch file yields to good results. You should check if distortion is considered. I record data with:

rosrun kinect2_bridge kinect2_bridge
rosrun image_view image_view image:=/kinect2/qhd/image_color_rect
rosbag record -O kinect_file /tf /kinect2/qhd/image_color_rect /kinect2/qhd/camera_info /kinect2/qhd/image_depth_rect

rgbdslam_kinect2.launch:

<launch>
<node pkg="rgbdslam" type="rgbdslam" name="rgbdslam" cwd="node" required="true" output="screen"> 
<!-- Input data settings-->
<param name="config/topic_image_mono"              value="/kinect2/qhd/image_color_rect"/>  
<param name="config/camera_info_topic"             value="/kinect2/qhd/camera_info"/>

<param name="config/topic_image_depth"             value="/kinect2/qhd/image_depth_rect"/>

<param name="config/topic_points"                  value=""/> <!--if empty, poincloud will be reconstructed from image and depth -->

<!-- These are the default values of some important parameters -->
<param name="config/feature_extractor_type"        value="SIFTGPU"/><!-- also available: SIFT, SIFTGPU, SURF, SURF128 (extended SURF), ORB. -->
<param name="config/feature_detector_type"         value="SIFTGPU"/><!-- also available: SIFT, SURF, GFTT (good features to track), ORB. -->
<param name="config/detector_grid_resolution"      value="3"/><!-- detect on a 3x3 grid (to spread ORB keypoints and parallelize SIFT and SURF) -->

<param name="config/optimizer_skip_step"           value="15"/><!-- optimize only every n-th frame -->
<param name="config/cloud_creation_skip_step"      value="2"/><!-- subsample the images' pixels (in both, width and height), when creating the cloud (and therefore reduce memory consumption) -->

<param name="config/backend_solver"                value="csparse"/><!-- pcg is faster and good for continuous online optimization, cholmod and csparse are better for offline optimization (without good initial guess)-->

<param name="config/pose_relative_to"              value="first"/><!-- optimize only a subset of the graph: "largest_loop" = Everything from the earliest matched frame to the current one. Use "first" to optimize the full graph, "inaffected" to optimize only the frames that were matched (not those inbetween for loops) -->

<param name="config/maximum_depth"           value="2"/>
<param name="config/subscriber_queue_size"         value="20"/>

<param name="config/min_sampled_candidates"        value="30"/><!-- Frame-to-frame comparisons to random frames (big loop closures) -->
<param name="config/predecessor_candidates"        value="20"/><!-- Frame-to-frame comparisons to sequential frames-->
<param name="config/neighbor_candidates"           value="20"/><!-- Frame-to-frame comparisons to graph neighbor frames-->
<param name="config/ransac_iterations"             value="140"/>

<param name="config/g2o_transformation_refinement"           value="1"/>
<param name="config/icp_method"           value="gicp"/>  <!-- icp, gicp ... -->

<!--
<param name="config/max_rotation_degree"           value="20"/>
<param name="config/max_translation_meter"           value="0.5"/>

<param name="config/min_matches"           value="30"/>   

<param name="config/min_translation_meter"           value="0.05"/>
<param name="config/min_rotation_degree"           value="3"/>
<param name="config/g2o_transformation_refinement"           value="2"/>
<param name="config/min_rotation_degree"           value="10"/>

<param name="config/matcher_type"         value="SIFTGPU"/>
 -->
</node>
</launch>