#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

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.

The names and functions do not change compared with hdl-graph-slam.

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.

key modifications

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()

Nodelets

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.

Parameters

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

Services

• /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.

Requirements

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.