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pointcloud_to_rangeimage package from pointcompress3d repo

pointcloud_to_rangeimage snnrmse velodyne velodyne_driver velodyne_laserscan velodyne_msgs velodyne_pcl velodyne_pointcloud

API Docs
Browse Code

Package Summary

Tags No category tags.
Version 0.1.0
License MIT
Build type CATKIN
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/pointcompress3d/pointcompress3d.git
VCS Type git
VCS Version main
Last Updated 2024-07-18
Dev Status UNMAINTAINED
CI status No Continuous Integration
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

Turns a 360 point cloud to range, azimuth and intensity image. Applies compression algorithms on these representations.

Additional Links

No additional links.

Maintainers

  • Yuchen Tao
  • Till Beemelmanns

Authors

  • Yuchen Tao
  • Till Beemelmanns
3dpcc/catkin_ws/src/pointcloud_to_rangeimage/README.md

RNN Based Point Cloud Compression

The idea is to losslessly convert a point cloud to range, azimuth and intensity images, then compress them using image compression methods.

Input Point Cloud Decompressed Point Cloud

Configuration

Initial settings of the Velodyne LiDAR sensor and parameters of the application can be set int the configuration file pointcloud_to_rangeimage.yaml. Default setting is based on Velodyne VLP-32C model.

point_cloud_to_rangeimage

Parameter Description Example
vlp_rpm RPM of the sensor 600.0
num_layers Number of laser layers in the sensor 32
firing_circle Time for one firing circle 0.000055296
elevation_offsets Elevation angle corresponding to each laser layer in ascending order see default setting
azimuth_offsets Azimuth offset corresponding to each laser layer with the same order as elevation_offsets see default setting
record_images Whether to record transformed images in a local folder false
record_path Path to store lidar compression dataset /home/rosuser/catkin_ws/dataset/
threshold Maximum range of point clouds 200

compression_method

Parameter | Description | Example ———— | ————- | ————- compression_method | Compression method: additive_lstm, oneshot_lstm, additive_gru, image_compression | additive_lstm

rnn_compression

Parameter | Description | Example ———— | ————- | ————- weights_path | Path to the model weights stored as .hdf5 | /catkin_ws/models/additive_lstm_32b_32iter.hdf5 bottleneck | Bottleneck size of the model | 32 num_iters | Number of iterations for compression | 32

image_compression

Parameter | Description | Example ———— | ————- | ————- image_compression_method | Select image compression method png or jpg | jpg show_debug_prints | Print debug prints during execution true or false | false

Usage

Dataset generation: Transform point clouds to images and store them in local folders.

  1. Specify record_path to store the images in the configuration file. The path should contain three sub-folders named azimuth, range and intensity. Set parameter record_images to true.
  2. Run roslaunch pointcloud_to_rangeimage compression.launch .
  3. In another terminal, play the rosbags to be transformed. The node pointcloud_to_rangeimage_node.cpp subscribes to the topic /velodyne_points with message type sensor_msgs/PointCloud2. The images will be stored in the three sub-folders. Raw Velodyne packet data is also supported with the help of the Velodyne driver, by including the launch file VLP-32C_driver.launch. Manual configuration regarding the sensor setup is needed.
  4. Optional: the dataset can be further splitted into train/val/test datasets using the library split-folders.
pip install split-folders
python
import splitfolders
splitfolders.ratio('path/to/parent/folder', output="path/to/output/folder", seed=1337, ratio=(.8, 0.1,0.1))

Online JPEG compression:

  1. Set parameter record_images to false.
  2. Set compression_method to ìmage_compression and choose desired compression quality here. See OpenCV imwrite flags.
  3. Run roslaunch pointcloud_to_rangeimage compression.launch . Path to the rosbag can be modified in the launch file.

Online RNN compression:

  1. Set parameter record_images to false.
  2. Set compression_method to additive_lstm or oneshot_lstm or additive_gru
  3. Set the parameters weights_path and bottleneck according to the picked model. Set num_iters to control the compressed message size.
  4. Run roslaunch pointcloud_to_rangeimage compression.launch. Path to the rosbag can be modified in the launch file.
CHANGELOG
No CHANGELOG found.

Wiki Tutorials

This package does not provide any links to tutorials in it's rosindex metadata. You can check on the ROS Wiki Tutorials page for the package.

Package Dependencies

Deps Name
roscpp
rospy
sensors_msgs
ros_bridge
dynamic_reconfigure
message_generation
image_transport
velodyne_pcl
catkin
message_runtime

System Dependencies

No direct system dependencies.

Dependant Packages

No known dependants.

Launch files

  • launch/compression.launch
  • launch/VLP-32C_driver.launch
    • -*- mode: XML -*-
      • calibration [default: $(find velodyne_pointcloud)/params/VeloView-VLP-32C.yaml]
      • device_ip [default: ]
      • frame_id [default: velodyne]
      • manager [default: $(arg frame_id)_nodelet_manager_ping]
      • max_range [default: 200.0]
      • min_range [default: 1.0]
      • pcap [default: ]
      • port [default: 2368]
      • read_fast [default: false]
      • read_once [default: false]
      • repeat_delay [default: 0.0]
      • rpm [default: 600.0]
      • gps_time [default: false]
      • cut_angle [default: -0.01]
      • timestamp_first_packet [default: false]
      • laserscan_ring [default: -1]
      • laserscan_resolution [default: 0.007]
      • organize_cloud [default: true]

Messages

Services

No service files found

Plugins

No plugins found.

Recent questions tagged pointcloud_to_rangeimage at Robotics Stack Exchange

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