depth_segmentation

This package provides geometric segmentation of depth images and an interface to semantic instance segmentation, where the output of a semantic instance segmentation of RGB images gets combined with the geometric instance segmentation. For the later case we assign each geometric segment a semantic label as well. TODO Add image(s)

If you are interested in a global segmentation map, please also take a look at voxblox-plusplus.

Installation

In your terminal, define the installed ROS version and name of the catkin workspace to use:

export ROS_VERSION=kinetic # (Ubuntu 16.04: kinetic, Ubuntu 18.04: melodic)
export CATKIN_WS=~/catkin_ws

If you don't have a catkin workspace yet, create a new one:

mkdir -p $CATKIN_WS/src && cd $CATKIN_WS
catkin init
catkin config --extend /opt/ros/$ROS_VERSION --merge-devel 
catkin config --cmake-args -DCMAKE_CXX_STANDARD=14 -DCMAKE_BUILD_TYPE=Release
wstool init src

Note: If you already have a catkin workspace, ensure that its devel space layout is merged. If you do catkin config in your workspace the output should include:

Devel Space Layout:          merged

Clone the depth_segmentation repository over HTTPS (no Github account required):

cd $CATKIN_WS/src
git clone --recurse-submodules https://github.com/ethz-asl/depth_segmentation.git

Alternatively, clone over SSH (Github account required):

cd $CATKIN_WS/src
git clone --recurse-submodules [email protected]:ethz-asl/depth_segmentation.git

Automatically fetch dependencies:

wstool merge -t . depth_segmentation/dependencies.rosinstall
wstool update

Build and source the packages:

catkin build depth_segmentation
source ../devel/setup.bash # (bash shell: ../devel/setup.bash,  zsh shell: ../devel/setup.zsh)

To compile it with Mask R-CNN support you'll need to set the WITH_MASKRCNNROS to ON in the CMakeLists.txt file:

set(WITH_MASKRCNNROS ON)

Usage

The two use cases can be started as described below.

Geometric Segmentation

If you only want geometric segmentation, use:

roslaunch depth_segmentation depth_segmenatation.launch

You'll need to adjust the ros topic names in the sensor_topics_file (by default this is in depth_segmentation//cfg/primesense_topics.yaml). The depth segmentation parameters can be adjusted via dynamic reconfigure or in the depth_segmentation_params_file directly.

Combined Geometric Segmentation with Semantics

To additionally run the semantic segmentation you can use this command:

roslaunch depth_segmentation semantic_instance_segmentation.launch

NOTE This only works if you have compiled depth_segmentation with Mask R-CNN enabled (WITH_MASKRCNNROS=ON).

Citing

If you use this, please cite:

• Fadri Furrer, Tonci Novkovic, Marius Fehr, Abel Gawel, Margarita Grinvald, Torsten Sattler, Roland Siegwart, Juan Nieto, Incremental Object Database: Building 3D Models from Multiple Partial Observations, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018. [PDF] [Video]

@INPROCEEDINGS{8594391, 
author={F. {Furrer} and T. {Novkovic} and M. {Fehr} and A. {Gawel} and M. {Grinvald} and T. {Sattler} and R. {Siegwart} and J. {Nieto}}, 
booktitle={2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)}, 
title={Incremental Object Database: Building 3D Models from Multiple Partial Observations}, 
year={2018},
pages={6835-6842}, 
keywords={feature extraction;image colour analysis;image reconstruction;image representation;image segmentation;mobile agents;object detection;solid modelling;multiple partial observations;incremental object database;indoor scenes;merged models;object model;observed instances;segmented RGB-D images;global segmentation map;3D models;mobile agent;Image segmentation;Databases;Three-dimensional displays;GSM;Shape;Image reconstruction;Solid modeling}, 
doi={10.1109/IROS.2018.8594391}, 
ISSN={2153-0866}, 
month={Oct},}

If you also use the semantic segmentation, additionally cite:

• Margarita Grinvald, Fadri Furrer, Tonci Novkovic, Jen Jen Chung, Cesar Cadena, Roland Siegwart, Juan Nieto, Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery, IEEE Robotics and Automation Letters, 2019. [PDF] [Video]

@article{grinvald2019volumetric,
  title={{Volumetric Instance-Aware Semantic Mapping and 3D Object Discovery}},
  author={Grinvald, Margarita and Furrer, Fadri and Novkovic, Tonci and Chung, Jen Jen and Cadena, Cesar and Siegwart, Roland and Nieto, Juan},
  journal={IEEE Robotics and Automation Letters},
  year={2019},
  note={Accepted}
}

License

The code is available under the BSD-3-Clause license.