Skip to content

Latest commit

 

History

History
326 lines (267 loc) · 14.5 KB

README.md

File metadata and controls

326 lines (267 loc) · 14.5 KB

EM-Fusion: Dynamic Object-Level SLAM With Probabilistic Data Association

Overview Image

This repository provides source code for EM-Fusion accompanying the following publication:

Michael Strecke and Joerg Stueckler, "EM-Fusion: Dynamic Object-Level SLAM With Probabilistic Data Association"
Presented at the IEEE/CVF International Conference on Computer Vision (ICCV) 2019, Seoul, Korea

Please see the project page for details.

If you use the source code provided in this repository for your research, please cite the corresponding publication as:

@InProceedings{strecke2019_emfusion,
  author      = {Michael Strecke and Joerg Stueckler},
  booktitle   = {2019 {IEEE}/{CVF} International Conference on Computer Vision ({ICCV})},
  title       = {{EM-Fusion}: Dynamic Object-Level {SLAM} With Probabilistic Data Association},
  year        = {2019},
  month       = {oct},
  publisher   = {{IEEE}},
  doi         = {10.1109/iccv.2019.00596},
}

Getting started

0. Install dependencies

Our code requires the following dependencies to be present on your system:

  • CUDA tested with version 10.0
  • CMake
  • boost (modules system, filesystem, program_options with development headers)
  • Python C API headers for calling python code from C++ and numpy as system-wide installation
  • Eigen
  • VTK for enabling the build of the OpenCV VIZ module for 3D visualizations
  • OpenCV with CUDA support (needs building from sources with the OpenCV-contrib modules) tested with version 4.3.0
  • Sophus
  • GTK 3 development package to make the OpenCV GUI compatible with Mask R-CNN

For CUDA, follow the setup instructions at the link above for your platform.

On Ubuntu 18.04, CMake, boost, Python, Eigen, and VTK can be installed from the default package repositories:

apt-get update && apt-get install cmake \
                libboost-system1.65-dev \
                libboost-filesystem1.65-dev \
                libboost-program-options1.65-dev \
                python3-dev python3-numpy virtualenv \
                libeigen3-dev \
                libvtk7-dev \
                libgtk-3-dev

OpenCV and Sophus need to be built from source. By using -DCMAKE_INSTALL_PREFIX=<path/to/install> you can install these libraries to a custom path. If you do so, set CMAKE_PREFIX_PATH accordingly when running the CMake configuration for our code.

Recommended CMake call for building OpenCV (disables some unnecessary parts):

cmake -DOPENCV_EXTRA_MODULES_PATH=<opencv_contrib_path>/modules \
      -DBUILD_TESTS=OFF \
      -DBUILD_PERF_TESTS=OFF \
      -DWITH_CUDA=ON \
      -DCUDA_GENERATION=Auto \
      <opencv_path>

1. Set up Mask R-CNN

Clone the Mask R-CNN repository to some location (<Mask_RCNN_DIR> in the following) and create and set up the virtual environment for Mask R-CNN in <Mask_RCNN_DIR>/venv:

cd <Mask_RCNN_DIR>
virtualenv -p /usr/bin/python3 venv
source venv/bin/activate

Afterwards, follow the instructions for setting up Mask R-CNN here. Make sure to install a TensorFlow version <2.0. In order to achieve this, edit the requirements.txt file and change the line tensorflow>=1.3.0 to tensorflow>=1.3.0,<2.0, as well as keras>=2.0.8 to keras>=2.0.8,<2.4.0 (keras 2.4.0 dropped support for Tensorflow 1.x, see here). For GPU support in TensorFlow, you might want to point it to a specific version compatible with you CUDA installation (see here for details). You also need to install pycocotools via pip install pycocotools.

Afterwards, you can leave the virtual environment with deactivate and return to the base directory of this repository.

Pretrained weights for Mask R-CNN will be downloaded automatically upon the first run of EM-Fusion or the preprocessing.

2. Build EM-Fusion

After installing all dependencies mentioned above and setting up Mask R-CNN, download the EM-Fusion code to a separate directory. In that directory, create a build directory, configure the build using CMake, and build the project:

mkdir build
cd build
cmake .. -DMASKRCNN_ROOT_DIR=<Mask_RCNN_DIR>
make -j$(nproc)

Depending on where you installed the dependencies, you might want to append -DCMAKE_PREFIX_PATH=<path/to/install> to the cmake call.

If you virtualenv for Mask R-CNN is not in <Mask_RCNN_DIR>/venv, append -DMASKRCNN_VENV_DIR=<path/to/venv> to the cmake call above.

3. Running the code

Change to the build folder for running the code (since the maskrcnn.py file is placed there this should be the working directory for running the code).

The main executable is called EM-Fusion and can be called with the following options:

$ ./EM-Fusion -h
EM-Fusion: Dynamic tracking and Mapping from RGB-D data:

One of these options is required:
  -t [ --tumdir ] arg       Directory containing RGB-D data in the TUM format
  -d [ --dir ] arg          Directory containing color and depth images

Possibly needed when using "--dir" above:
  --colordir arg (=colour)  Subdirectory containing color images named 
                            Color*.png. Needed if different from "colour"
  --depthdir arg (=depth)   Subdirectory containing depth images named 
                            Depth*.exr. Needed if different from "depth"

Optional inputs:
  -h [ --help ]             Print this help
  -e [ --exportdir ] arg    Directory for storing results
  --export-frame-meshes     Whether to export meshes for every frame. Needs a 
                            lot of RAM if there are many objects.
  --export-volumes          Whether to output TSDF volumes with weights and 
                            foreground probabilities. Needs a lot of RAM if 
                            there are many objects.
  --background              Whether to run this program without live output 
                            (without -e it won'tbe possible to examine results)
  --3d-vis                  Whether to show 3D visualizations with meshes and 
                            bounding boxes
  -c [ --configfile ] arg   Path to a configuration file containing experiment 
                            parameters
  -m [ --maskdir ] arg      Directory containing preprocessed Mask R-CNN 
                            results

The -t and -d flags load data stored in the format of the TUM RGB-D SLAM Dataset and the Co-Fusion dataset, respectively.

--colordir or --depthdir are needed when the color or depth subfolders of files in the Co-Fusion format are not named colour and depth, respectively. (E.g. for the synthetic sequences, --depthdir depth_noise was used in our experiments.)

The -e flag can be used for storing results to a folder (will be overwritten if it exists). We provide options for optionally writing meshes for each frame and the final TSDF volumes to disk. Since these are buffered in RAM until the program ends, this might take a lot of memory.

--background allows running the program without direct visual output (no X server) required. You should save results with -e to examine them.

--3d-vis enables 3D visualization. This creates a window that displays meshes generated by marching cubes and the bounding boxes of objects. This slows down the processing since marching cubes is run on every object in every frame. This flag cannot be combined with --background and will be ignored if you try to do so anyways.

The -c flag allows giving experiment-specific parameters in a config file. Check data.h for documentation of the parameters and config/ for example config files. config/default.cfg specifies default parameters that are also set automatically in data.h and can be used as a reference guide on how to specify parameters.

The -m flag allows loading preprocessed masks. (See below.)

3.1 Preprocessing with Mask R-CNN (optional)

By default, the EM-Fusion executable will attempt to run Mask R-CNN sequentially as was done in the paper experiments. Since Tensorflow takes around 4-5GB of GPU memory for this, you might run into issues with available GPU memory. To avoid this, we provide the program preprocess_masks, which lets you preprocess and save the Mask R-CNN results in a format readable for EM-Fusion.

$ ./preprocess_masks -h
Preprocess RGB-D datasets with Mask R-CNN:

One input option and the output option is required:
  -t [ --tumdir ] arg       Directory containing RGB-D data in the TUM format
  -d [ --dir ] arg          Directory containing color and depth images
  -m [ --maskdir ] arg      Where to store the generated masks

Possibly needed when using "--dir" above:
  --colordir arg (=colour)  Subdirectory containing color images named 
                            Color*.png. Needed if different from "colour"
  --depthdir arg (=depth)   Subdirectory containing depth images named 
                            Depth*.exr. Needed if different from "depth"

Optional inputs:
  -h [ --help ]             Print this help
  -c [ --configfile ] arg   Path to a configuration file containing experiment 
                            parameters

Most parameters are the same as for EM-Fusion. The -m flag is now the output folder and required for running this program. The only parameters from the configuration file that are used in this program are the Mask R-CNN frequency and the options FILTER_CLASSES and STATIC_OBJECTS that allow to only process some classes or disregard some classes as static.

3.2 Reducing volume resolution (optional)

If you still run out of GPU memory after following the previous section, the main source for savings in GPU memory consumption is the background TSDF volume resolution. Try reducing it in the config file (remember to adjust the voxel size accordingly so the overall volume dimension stays the same). This change might influence the results in terms of level of detail for the background volume and tracking accuracies.

Reproducing paper results

For reproducing the results from the paper, you can run the code on sequences from the Co-Fusion dataset and the TUM RGB-D SLAM Dataset. The following configurations were used in the paper's experiments:

  • For car4-full, we used the default configuration
  • For room4-full, the volume size and starting pose needed to be adjusted as given in room4.cfg
  • For the real-world scenes from Co-Fusion, the pattern on a wall caused spurious 'umbrella' detections that we excluded as static objects in co-fusion-real.cfg
  • For the robust background tracking experiment on the TUM RGB-D benchmark, we only detect 'person' objects and disable their visualization in the rendered output as set up in tum.cfg

A more detailed guide on how to run EM-Fusion can be found here.

We provide scripts to automatically reproduce paper results consisting of the following parts:

Run EM-Fusion

run_exps.sh runs EM-Fusion on the Co-Fusion and TUM RGB-D datasets.

For this to work, you will need to download the synthetic archives car4-full.tar.gz and room4-full.tar.gz from this site. Extract both archives to a folder (CO-FUSION_FOLDER in the following) in subdirectories "car4-full" and "room4-full", respectively.

Furthermore, download the TUM RGB-D dataset from here (at least the fr3/sitting* and fr3/walking* scenes) and place it in a folder (TUM_FOLDER in the following). The fr3/{sitting,walking}* scenes should be placed in TUM_FOLDER/freiburg3/. Also make sure to run the association script for each scene like this:

python associate.py rgb.txt depth.txt > associations.txt

You can then execute the following to run EM-Fusion and write the results to an OUTPUT_FOLDER:

./run_exps.sh $CO-FUSION_FOLDER $TUM_FOLDER $OUTPUT_FOLDER

Evaluate results on Co-Fusion

For evaluating performance on the Co-Fusion datasets, you will need the program convert_poses from the dataset-tools repository and the evaluate_ate.py and evaluate_rpe.py script from here. Make sure to set the correct locations for these in eval_co-fusion.sh.

Then, after running EM-Fusion with the above script, you can run

./eval_co-fusion.sh $OUTPUT_FOLDER $CO-FUSION_FOLDER

to evaluate EM-Fusion's performance.

Evaluate on TUM RGB-D dataset

Adapt the path for the evaluation scripts from above in eval_tum.sh.

Then, you can run

./eval_tum.sh $OUTPUT_FOLDER $TUM_FOLDER

to evaluate EM-Fusion for robust static background tracking.

Output folder structure

|-co-fusion
|  |-car4
|  |   |-multiple folders: containing weights/etc used for figures in the paper
|  |   |-mesh*.ply: final mesh models for all objects
|  |   |-poses*.txt: files for the internal object poses
|  |   |-poses*-corrected.txt: files containing poses corrected for possbly resized objects
|  |   |-poses*-mapped-origin.txt: aligned poses from convert_poses
|  |   |-{ate,rpe}*.txt: files containing the numerical errors for the objects
|  |   `-{ate,rpe}*.pdf: visualizations of the errors
|  `-room4 with the same structure as car4
`-tum
   `-f3_* folders for each dataset
        |-folders as above
        |-poses-files as above
        |-poses-cam-ts.txt with frame numbers replaced by timestamps
        `-ate and rpe results and visualizations for the camera trajectory

License

EM-Fusion has been developed at the Embodied Vision Group at the Max Planck Institute for Intelligent Systems, Germany. The open-source version is licensed under the GNU General Public License v3 (GPLv3).

For commercial inquiries, please send email to ev-license@tue.mpg.de.