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#FD-SLAM This is an open source ROS package for real-time 6DOF SLAM using a 3D LIDAR.

It is based on hdl_graph_slam and the steps to run our system are same with hdl-graph-slam.

We also release UGICP.

Our paper is under review and will be released soon. FD-SLAM: Feature&Distribution-based 3D LiDAR SLAM method based on Surface Representation Refinement

hdl_graph_slam is an open source ROS package for real-time 6DOF SLAM using a 3D LIDAR. It is based on 3D Graph SLAM with NDT scan matching-based odometry estimation and loop detection. It also supports several graph constraints, such as GPS, IMU acceleration (gravity vector), IMU orientation (magnetic sensor), and floor plane (detected in a point cloud). We have tested this package with Velodyne (HDL32e, VLP16) and RoboSense (16 channels) sensors in indoor and outdoor environments.

We use a novel feature-based Lidar odometry for fast scan-matching, and use a proposed UGICP for keyframe matching. The backend in hdl-graph-slam is reused. We have tested this package with Velodyne (HDL32e, HDL64,VLP16) and Ouster64 sensors in indoor and outdoor environments. The corresponding configure launch files are provided.

src/hdl_graph_slam/imageProjection.cpp and src/hdl_graph_slam/featureAssociation.cpp are added for fast scan matching

As for the fast-gicp, key modifications are taken place in fast_gicp_impl.cpp----calculate_covariances()

FD-slam consists of four nodelets.

• prefiltering_nodelet
• scan_matching_odometry_nodelet
• floor_detection_nodelet
• hdl_graph_slam_nodelet

The input point cloud is first downsampled by prefiltering_nodelet, and then passed to the next nodelets. While scan_matching_odometry_nodelet estimates the sensor pose by iteratively applying a scan matching between consecutive frames (i.e., odometry estimation), floor_detection_nodelet detects floor planes by RANSAC. The estimated odometry and the detected floor planes are sent to hdl_graph_slam. To compensate the accumulated error of the scan matching, it performs loop detection and optimizes a pose graph which takes various constraints into account.

All the configurable parameters are listed in launch/**.launch as ros params.

• /hdl_graph_slam/dump (hdl_graph_slam/DumpGraph)
  • save all the internal data (point clouds, floor coeffs, odoms, and pose graph) to a directory.
• /hdl_graph_slam/save_map (hdl_graph_slam/SaveMap)
  • save the generated map as a PCD file.

hdl_graph_slam requires the following libraries:

• OpenMP
• PCL
• g2o
• suitesparse

The following ROS packages are required:

• geodesy
• nmea_msgs
• pcl_ros
• ndt_omp
• U_gicp This is modified based on fast_gicp by us. We use UGICP for keyframe matching.