This repo is provided to assess the performance of OHM against several other voxel or octree based libraries.

• ROS
  • See http://wiki.ros.org/ROS/Installation
• CUDA - see ohm dependencies
• OpenCL - see ohm dependencies
• colcon and ninja for building (python3-colcon-common-extensions, python3-colcon-mixin, ninja-build)
  • sudo apt install -y python3-colcon-common-extensions python3-colcon-mixin ninja-build
• rosdep to install other dependencies.

The following bash code can be use to install the dependencies.

rosdep installation

# ROS Noetic
sudo apt install -y python3-rosdep
# ROS Melodic and earlier
sudo apt install -y python-rosdep
# ROS Melodic or Noetic
sudo rosdep init
rosdep update

Other dependencies:

sudo apt install -y python3-colcon-common-extensions python3-colcon-mixin ninja-build
sudo apt install -y cmake zlib1g-dev libglm-dev libtbb-dev libpdal-dev doxygen
# From ohm_assay root directory
rosdep install --from-paths src --ignore-src -r -y

Note: with Ubuntu 20.04 and ROS noetic, this will fail to install the following python-requests. This should be python3-requests on Ubuntu 20.04. The rosdep installation steps above ensure python3-requests is already installed.

See also the dependencies for the ohm project.

1. Set the environment variable COLCON_HOME=./.colcon
2. Source the ros setup script: e.g., source /opt/ros/melodic/setup.bash
3. colcon build --mixin <build-type>

Available build types:

• rel-with-deb
• release
• default

Omitting the build type mixin defaults to release

1. Set the environment variable COLCON_HOME=./.colcon
2. Source the ros setup script: e.g., source /opt/ros/melodic/setup.bash
3. Start VSCode
4. Run the VSCode command Tasks: Run Build Task (shortcut: Ctrl+Shift+B or Ctrl+/ depending on bindings)
   • Select build task and type

Example data files are available for download from TODO(KS). This includes data in ROS bag format containing online SLAM odometry and lidar sensor data. These files simulate running an online OHM solution. Additionally, there are pre-processed, globally optimal point cloud and trajectory data files.

To run the online solution:

1. Source the workspace setup script: e.g., source install/setup.bash
2. Launch ohmassay playback: e.g., roslaunch ohmassay_launch playback.launch "bags:=$(echo /path/to/bags/*.bag)" [options]

See playback.launch for a list of options and arguments.

For offline processing, OHM assay contains the following programs:

To run the offline solutions

1. Source the workspace setup script: e.g., source install/setup.bash
2. Launch <lib>pop<type>: e.g., ohmpopcuda point_cloud.ply trajectory.txt [options]"

Run <lib>pop<type> --help for a list of options and arguments.

Five test data sets are available via CSIRO Data Access Portal. This location contains two types of data archives suffixed _bags.zip and _cloud.zip. The _bags.zip files expand to ROS melodic generated bag files containing point cloud and trajectory messages generated from approximately five minutes of operation. The _cloud.zip files contain a post processed PLY point cloud and text based trajectory for the same five minute period. The flatpack and hovermap data sets were captured under direct operator control, while all other data are captured during autonomous operation. All data sets except flatpack are captured with a lidar mounted to a rotating encoder.

The data set environments are described below.

* Hovermap data set courtesy of Emesent. All other data sets captured by the CSIRO Robotics and Autonomous Systems group.

Other data sets may also be used provided they adhere to the requirements outlined in the sections below.

OHM assay requires the following data from a ros bag data source:

• A tf tree containing a moving frame which represents the lidar sensor.
• A PointCloud2 topic containing lidar sensor data in odometry or map frame.

OHM assay supports individual timestamps for points in the PointCloud2 message and will use these for OHM algorithms if present. Point times are read from the first available point cloud field matching one of the following labels: time, times, timestamp.

The OHM assay map population programs require a point cloud with a trajectory or raycloud generated from a SLAM algorithm. The point cloud and trajectory are expected to be globally optimal SLAM solutions and must have correlated timestamps. Each point in the point cloud must have a timestamp with (double precision is recommended) and each point in the trajectory must have a similar timestamp from which the sensor location for each point can be inferred.

The cloud files must be PLY format, while a trajectory can be either a text format (see below) or a PLY series of trajectory points.

A trajectory may be omitted when the cloud file is a raycloud PLY file.

Valid trajectory text formats are:

• Space delimited, %time x y z, one entry per line, column names ignored. Additional fields are allowed, but are ignored.
• Point cloud XYZ ASCII file format, column names dictate content.
• Point cloud PLY format.

Below is a list of point cloud fields used by OHM.

* Any of the time field names are acceptable. The first match is used as the timestamp field.
** All three normal channels are required for a ray cloud with the normal stored as a vector from the sample point back to the sensor (not unit length).
*** Intensity is required for the Normal Distribution Transform Traversability Model (NDT-TM).