ndt_omp

This package provides an OpenMP-boosted Normal Distributions Transform (and GICP) algorithm derived from pcl. The NDT algorithm is modified to be SSE-friendly and multi-threaded. It can run up to 10 times faster than its original version in pcl.

multigrid ndt_omp

TIER IV has developed an extended version of ndt_omp for dynamic map loading functionality. The difference from the ndt_omp is as follows:

• Instead of setInputTarget interface, multigrid_ndt_omp provides addTarget and removeTarget for more flexible target inputs.
• Only RadiusSearch is supported as a search method (getNeighborhoodAtPointX methods are disabled).

Benchmark (on Core i7-6700K)

$ roscd ndt_omp/data
$ rosrun ndt_omp align 251370668.pcd 251371071.pcd
--- pcl::NDT ---
single : 282.222[msec]
10times: 2921.92[msec]
fitness: 0.213937

--- pclomp::NDT (KDTREE, 1 threads) ---
single : 207.697[msec]
10times: 2059.19[msec]
fitness: 0.213937

--- pclomp::NDT (DIRECT7, 1 threads) ---
single : 139.433[msec]
10times: 1356.79[msec]
fitness: 0.214205

--- pclomp::NDT (DIRECT1, 1 threads) ---
single : 34.6418[msec]
10times: 317.03[msec]
fitness: 0.208511

--- pclomp::NDT (KDTREE, 8 threads) ---
single : 54.9903[msec]
10times: 500.51[msec]
fitness: 0.213937

--- pclomp::NDT (DIRECT7, 8 threads) ---
single : 63.1442[msec]
10times: 343.336[msec]
fitness: 0.214205

--- pclomp::NDT (DIRECT1, 8 threads) ---
single : 17.2353[msec]
10times: 100.025[msec]
fitness: 0.208511

Several methods for neighbor voxel search are implemented. If you select pclomp::KDTREE, results will be completely same as the original pcl::NDT. We recommend to use pclomp::DIRECT7 which is faster and stable. If you need extremely fast registration, choose pclomp::DIRECT1, but it might be a bit unstable.

Red: target, Green: source, Blue: aligned

Related packages

• ndt_omp
• fast_gicp

Regression Test

Preparation

You can use script/convert_rosbag_to_test_data.py to convert a rosbag to regression test data.

The regression test data should be placed in ./regression_test_data/input directory.

A sample data is here.

The sample data is recorded by AWSIM, so the corresponding map is nishishinjuku_autoware_map.

The directory structure should be like this:

./regression_test_data/
└── input
    ├── kinematic_state.csv
    ├── pointcloud_map.pcd  # means map.pcd
    └── sensor_pcd
        ├── pointcloud_00000000.pcd
        ├── pointcloud_00000001.pcd
        ├── pointcloud_00000002.pcd
        ├── ...
        ├── pointcloud_00000817.pcd
        ├── pointcloud_00000818.pcd
        └── pointcloud_00000819.pcd

build

mkdir build
cd build
cmake ..
make -j

Run

./regression_test ../regression_test_data/input ../regression_test_data/output

Check

python3 script/compare_regression_test_result.py ../regression_test_data/output ../regression_test_data/reference_output