Author: Zhixiang Min
Contact: [email protected]

VOLDOR-SLAM is a real-time dense-indirect SLAM system takes dense optical flows as input that supports monocular, stereo and RGB-D video sequence. The system logic is written in native python, which is friendly to modify and get started.

VOLDOR-SLAM: For the times when feature-based or direct methods are not good enough
Zhixiang Min, Enrique Dunn
ICRA 2021 [paper] [video]

VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residuals
Zhixiang Min, Yiding Yang, Enrique Dunn
CVPR 2020 [paper] [video]

Our system is built with cuda, cython and python. We will support the compatibility under the following configurations:

Windows 10 + Visual Studio 2017  /  Ubuntu 18.04
CUDA >= 9.0
Python 3.6.X
OpenCV 3.4.X
Ceres 2.0    (Optional for mapping)
pyDBoW3   (Optional for loop closure)

• Install the python package dependencies via pip.

  cd slam_py/install  
  pip install -r .\requirements.txt  

• For windows users, we provide prebuilt binaries. Download and copy the binaries files into the demo folder. You may also need install VC++ runtime library.

• OpenCV (Required)

  1. Download and setup OpenCV 3.4.X.
  2. Copy opencv_worldxxx.dll from the opencv workspace to the demo folder.

• PyOpenGL (Optional for viewer)

  1. PyOpenGL installed from pip requires additional dll file to be configured in PATH. If you meet OpenGL.error.NullFunctionError while launching the viewer, try install PyOpenGL from wheels PyOpenGL-wheels.

• Ceres (Optional for mapping)

  1. Download and follow the instructions to build Ceres with SuiteSparse. (Painful)
  2. Copy ceres.dll, glog.dll, libblas.dll, libgcc_s_sjlj-1.dll, libgfortran-3.dll, liblapack.dll, libquadmath-0.dll to the demo folder. They are all ceres dependencies. You can find them under Ceres and SuiteSparse workspace.

• pyDBoW3 (Optional for loop closure. Prerequisite building full SLAM)

  1. Download and follow the instructions to build pyDBoW3. The library depends on boost and opencv. You will need to provide/change the library path in the build script.
  2. You will get pyDBoW3.pyd when you succeeded. Copy it to the demo folder.
  3. The vocabulary file ORBvoc.bin already included in the demo folder.

• Build VO Only:

  1. Open and edit slam_py/setup_win_vo.py. You will need to provide/change opencv_include_dir, opencv_lib_dir and opencv_lib_name according to your opencv installation.
  2. Run the following code to build.

     cd slam_py/install  
     python setup_win_vo.py build_ext -i  

  3. If succeed, gpu-kernels.dll and pyvoldor_vo.xxx.pyd will appear in the install folder. Copy them to the demo folder.

• Build Full SLAM Pipeline:

  1. Open and edit slam_py/setup_win_full.py. You will need to provide/change opencv_include_dir, opencv_lib_dir, opencv_lib_name, ceres_include_dirs and ceres_lib_dirs according to your opencv and ceres installations.
  2. Run the following code to build. (No need building VO module first)

     cd slam_py/install  
     python setup_win_full.py build_ext -i  

  3. If succeed, gpu-kernels.dll and pyvoldor_full.xxx.pyd will appear in the install folder. Copy them to the demo folder.

• OpenCV (Required)

  1. If using sudo apt install libopencv-dev, be aware of its version that we only tested under 3.4.X, though we found most versions work fine.
  2. If build from source, remember running sudo make install.

• PyOpenGL (Optional for viewer)

  1. sudo apt install python-opengl

• Ceres (Optional for mapping)

  1. Build Ceres with SuiteSparse follow the instructions. Remember running sudo make install.

• pyDBoW3 (Optional for loop closure. Prerequisite building full SLAM)

  1. Download and follow the instructions to build pyDBoW3.
  2. You will get pyDBoW3.so when you succeed. Copy it to the demo folder.
  3. The vocabulary file ORBvoc.bin already included in the demo folder.

• Build VO Only:

  1. Run the following code to build.

     cd slam_py/install  
     python setup_linux_vo.py build_ext -i

  2. If succeed, libgpu-kernels.so and pyvoldor_vo.xxx.so will appear in the install folder. Copy them to the demo folder.

• Build Full SLAM Pipeline:

  1. Run the following code to build. (No need building VO module first)

     cd slam_py/install  
     python setup_linux_full.py build_ext -i

  2. If succeed, libgpu-kernels.so and pyvoldor_full.xxx.so will appear in the install folder. Copy them to the demo folder.

Our method takes three inputs. You may download demo data to run and play.

Optical flows are REQUIRED for visual odometry. We recommend MaskFlowNet or PWC-Net as the optical flow estimator.
Supported format: .flo

Disparity maps are OPTIONAL input only present when stereo pairs or depth sensors are available. For stereo pairs, we recommend using the same optical flow estimator running from left to right. For depth sensors, you need to set a virtual baseline value to convert the depth to disparity as input.
Supported format: .flo, .png (x256 gray-16bit)

RGB images are OPTIONAL input for adding photo-consistency to enhance the frame alignment.
Supported format: all image formats supported by opencv

cd demo
python demo.py --help
python demo.py 
--fx 320 --fy 320 --cx 320 --cy 240 --bf 80 
--flow_dir 'path_to_flow' 
--img_dir 'path_to_img' 
--disp_dir 'path_to_disp' 
--mode stereo 
--enable_mapping 
--enable_loop_closure './ORBvoc.bin' 
--save_poses './poses.txt' 
--save_depths './depths' 

More details can be found in the comments of the demo script. In demo data, we also provided bash commands in run_demo.txt under each data instance folder.

Viewer Control Manual

> q     ->   Exit  
> m     ->   Save point cloud to .ply  
> r     ->   Reset view  
> h     ->   Hide cameras / links  
> f     ->   Follow the current camera  
> w/s   ->   Increase/Decrease scene pixel size  
> d/a   ->   Increase/Decrease scene pixel density  
> x/z   ->   Increase/Decrease scene pixel depth range  
> Mouse scroll up/down         ->   Zoom in/out  
> Mouse drag with left down    ->   Scene rotation  
> Mouse drag with right down   ->   Scene translation  

Viewer Range. When running with monocular capture, if the world scale is unfortunately initialized too large, the viewer may not display many pixels, which can be adjusted using x/z keys.

Input Frame Rate. Our framework favors a modest baseline between frames to let the optical flows being informative. For high frame rate videos, consider downsampling the frame rate based on appearance changing. (E.g. KITTI/TartanAir @ 10Hz, TUM-RGBD @ 3Hz.)

I/O BottleNeck. If you observe choppy GPU load or the latest map on the viewer colored gray, it probably means the disk I/O is bottlenecking the performance. This will affect the accuracy by blocking the local mapping while VO keeps running. Consider move your data to SSD disk or you may restart the software a few times that your disk will cache more data.

Parameter Tuning. Usually default parameters work for all. You may try loosing the loop closure thresholds to detect more loop closures. For very challenging dataset, if the full pipeline is not stable, try disable local mapping. For tuning VO parameters, pass parameters using slam.voldor_user_config. All available parameters can be found in voldor/config.h with descriptions. For SLAM parameters, check the __init__ method of VOLDOR_SLAM.

What is mono-scaled mode? Mono-scaled mode is to use the disparity map only for correcting the world scale.

The software is released under the attached LICENSE file.
Parts of the software rely on the following open source projects:
LambdaTwist, OpenCV, pyDBoW3, DBoW3, Ceres, SuiteSparse, Eigen, svd3, PyOpenGL