Elastic Bridge for ROS Noetic

This fork of the repo includes the following stuff on top of the original functionalities:

Dockerfile which should help specify dependencies and commit IDs. Note: Current Dockerfile is bulky and can be stripped down according to what is needed.
Compatibility with the changes in the newer version of ElasticFusion, specifically with Sophus.
Stamped pose publishing to /elastic_camera_pose, and recording script in evaluation/generate_pose_txt_files.py to record poses in TUM format for ATE evaluation.
Depth scale as a launch argument to account for non-standard depth scaling, as is present in the TUM RGB-D dataset.

The original README follows:

The elastic_bridge package integrates ElasticFusion by mp3guy into ROS (Robot Operating System).

https://github.com/mp3guy/ElasticFusion

The package is non-interactive. It only depends on the ElasticFusion Core module, instead of the GUI module. Moreover, it can be used with any RGB-D sensor with a ROS driver, as it does not depend on OpenNI.

Dependencies (in addition to ElasticFusion dependencies):

ROS (Robot Operating System)
PCL (Point Cloud Library)
Intel Realsense ROS package
init_fake_opengl_context: badly-named package to initialize a windowless OpenGL context (https://github.com/RMonica/init_fake_opengl_context)

The package was tested on ROS Noetic (Ubuntu 20.04) with an Intel RealSense.

An optional patch may be applied to the ElasticFusion source code. The patch assigns an unique identifier (GUID) to each surfel, so that single surfels can be tracked during the 3D reconstruction process. For example, you may keep track of extra properties for each surfel in an external ROS node. See "GUID patch" below for more information.

Installation

Download this repository, elastic_bridge, into your ROS workspace
Download init_fake_opengl_context into your ROS workspace, from https://github.com/RMonica/init_fake_opengl_context.
Download ElasticFusion from https://github.com/mp3guy/ElasticFusion into [...]elastic_bridge/deps/ElasticFusion.
Download Pangolin from https://github.com/stevenlovegrove/Pangolin into [...]elastic_bridge/deps/ElasticFusion/deps/Pangolin.
Compile Pangolin:

cd [...]elastic_bridge/deps/ElasticFusion/deps/Pangolin
mkdir build
cd build  
cmake .. -DCMAKE_BUILD_TYPE=Release
make

Optional: apply the GUID patch

cd [...]elastic_bridge/deps/ElasticFusion
patch -p1 -i ../../patches/guid.patch

Compile ElasticFusion Core module

cd [...]elastic_bridge/deps/ElasticFusion/Core
mkdir build
cd build
cmake ../src
make

Update the EFUSION_ROOT variable in the CMakeLists.txt file to point to the directory where ElasticFusion is.
Compile the package (catkin build is recommended instead of catkin_make):

catkin build elastic_bridge

Usage

An example launch file for Kinect v1 is provided in the launch folder. The main node is elastic_node.

Set parameters TOPIC_IMAGE_COLOR to the color image topic, TOPIC_IMAGE_DEPTH to the depth image topic, and TOPIC_CAMERA_INFO to the camera info topic. The node waits for the first camera info before creating the ElasticFusion instance, as ElasticFusion cannot be initialized before image width and height are known.

By default, the node starts in suspended state. Set AUTOSTART to true if the node should start 3D reconstruction immediately.

To suspend the node while it is running, send "true" to the suspend ROS service: rosservice call /elastic_node/suspend true

To un-suspend the node while it is paused, send "false" to the suspend ROS service: rosservice call /elastic_node/suspend false

During execution, the node publishes the current 3D reconstruction image in /elastic_current_view. Current sensor pose, as estimated by egomotion tracking, is published to TF frame /camera_frame, with reference /first_frame, corresponding to the first camera pose.

It is possible to provide an external tracking source by setting parameter TF_POSE_ALWAYS to true. The transformation is read by default between the TF frames /world and /robot.

The surfel-based 3D reconstruction can be downloaded as a point cloud of pcl::PointSurfel. For this, the action /save_pcl may be called. An example is provided in the node save_pcl, compiled from src/save_pcl.cpp.

Parameters

TOPIC_IMAGE_COLOR (string): color image topic.
TOPIC_IMAGE_DEPTH (string): depth image topic.
TOPIC_CAMERA_INFO (string): camera info topic.
WORLD_FRAME (string): published TF reference frame (default: /first_frame)
CAMERA_FRAME (string): published TF camera frame (default: /camera_frame)
AUTOSTART (bool): if false, the node is in suspended state at startup (default: false)
DISPLAY_NAME (string): X display for the OpenGL context (default: auto-detect)
BLACK_TO_NAN (bool): some cameras set invalid RGB pixels as pure black, if this option is set such pixels are ignored even if they have valid depth (default: false)
PERIODIC_WORLD_PUBLICATION (int): every PERIODIC_WORLD_PUBLICATION frames, the surfel cloud is published as specified by TOPIC_PERIODIC_WORLD_PUB (default 0: disabled)
TOPIC_PERIODIC_WORLD_PUB (string): topic in which the surfel cloud should be published as sensor_msgs/PointCloud2 (default: /elastic_world_pub)
TOPIC_CURRENT_VIEW (string): at each frame, a RGB image showing the view as predicted from the current sensor pose is published to this topic (default: /elastic_current_view)
TOPIC_FRAME_STATE (string): at each frame, a message of type msg/FrameState.msg is published, which contains additional information for each pixel of the predicted view, such as position, normal, etc. (default: /elastic_frame_state_stable)
TF_POSE_ALWAYS (bool): if true, sensor pose is read from TF (default: false)
TF_POSE_FIRST (bool): if true, the sensor pose is read from TF only for the first sensor frame (default: false)
TF_INPUT_WORLD_FRAME (string): set TF reference frame for external sensor pose tracking (default: world)
TF_INPUT_CAMERA_FRAME (string): set TF camera frame for external sensor pose tracking (default: robot)
SAVE_PCL_ACTION (string): action name to retrieve the surfel cloud (default: /save_pcl)

GUID patch

Surfels can be reordered by ElasticFusion during execution. With the GUID patch, the node also tracks each surfel by assigning an unique ID to each of them. Two kind of UIDs are tracked:

A LUID (32-bit integer) identifies a surfel as long as it exists, but it can be re-assigned to new surfels if it is destroyed
A GUID (64-bit integer) identifies a surfel and it is never reused even if the surfel is destroyed

LUIDs and GUIDs are returned in the frame state, one for each pixel in the image, in the topic as defined by parameter TOPIC_FRAME_STATE. The message type FrameState.msg contains, among other things:

guid: array of GUIDs, one for each pixel
luid: array of LUIDs, one for each pixel
luid_removed: LUIDs of the surfels destroyed in this frame, which are no longer valid and may be reused in the next frame.
max_luid: maximum currently existing LUID

GUIDs and LUIDs are also returned by the save_pcl action, one for each surfel (see action definition action/downloadPointcloud2.action.

Acknowledgments

The original elastic_bridge package was written by Andrea Pagani during his bachelor thesis in Computer, Electronics and Telecommunication Engineering at University of Parma, Italy, in 2017-2018. The updates were made by Anja Sheppard in 2021 to make it run on ROS Noetic/Ubuntu 20.04.

License

snt-arg/elastic_bridge

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