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MonoRec SLAM

In this project, our method:

  • Alleviate the tracking algorithm from using matches that belong to dynamic objects, in most cases achieving a higher accuracy in evaluation.

  • Alleviate the mapping algorithm from including moving objects as part of the 3D map, obtaining a more static map of scenes.

  • Achieve a good balance between real-time capability and dynamic object masking.

Benchmarks

Absolute trajectory RMSE (m) for ORB-SLAM3, DynaSLAM and MonoRecSLAM on the KITTI dataset

Sequence ORB-SLAM DynaSLAM MonoRecSLAM
00 5.33 7.55 6.71
04 1.62 0.97 1.39
05 4.85 4.60 4.60
07 2.26 2.36 2.09

Installation

Click to view details 1. Download this repo and initialize all submodules 
```bash
git clone [email protected]:silvery107/monorec-slam-ros.git
git submodule update --init
```
Or use `--recurse` option to clone submodules at the same time.

  1. Create a conda environment

    conda env create -f environment.yml

  2. Download pretrained MonoRec model and install MonoRec as a submodule

    cd modules/MonoRec && ./download_models.sh
pip install -e .

  3. Download the KITTI odometry dataset

    To setup KITTI Odometry, download the color images and calibration files from the official website (around 65 GB). Then unzip the color images and calibration files into data folder.

    For evaluation, MonoRec uses the poses estimated by Deep Virtual Stereo Odometry (DVSO). They can be downloaded from here and should be placed under data/{kitti_path}/poses_dso. This folder structure is ensured when unpacking the zip file in the {kitti_path} directory.

  4. Install evo for SLAM trajectory evaluation

    pip install evo --upgrade --no-binary evo

  5. Install ORB-SLAM3 according to its instructions and remember to install it on the c++14_comp branch. Install its ROS interface as well.

  6. The dataset structure should be like this

    data
├── kitti
│   ├── poses
│   ├── poses_dvso
│   └── sequences
|       └── ...
└── tum
    ├── rgbd_dataset_freiburg3_walking_xyz
    └── ...

Quick Start

Here we take the sequence 07 of KITTI dataset as an example.

Run with ROS

The combined implementation of MonoRec SLAM is done by pumping masked image onto the /camera/image_raw ROS topic and utilizing the ROS interface from ORB-SLAM3 to perform SLAM.

# terminal 1
roscore
# terminal 2
rosrun ORB_SLAM3 Mono Vocabulary/ORBvoc.txt Examples/Monocular/KITTI04-12.yaml 
# terminal 3
python src/monorec_ros.py --dataset kitti --seq 7

or ...

Dataset Preprocessing

  1. Run MonoRec model to get binary masks of moving objects

    python src/generate_mask.py --dataset kitti --seq 7

  2. Prepare masked images from masks

    python src/process_image.py --dataset kitti --seq 7

SLAM with ORB-SLAM3

  1. Run SLAM on precessed dataset, e.g. 
    cd modules/ORB_SLAM3
./Examples/Monocular/mono_kitti ./Vocabulary/ORBvoc.txt ./Examples/Monocular/{config}.yaml ../../data/kitti/squences/07
  2. Copy the resulted KeyFrameTrajectory.txt onto e.g. results/kitti/07/

Trajectory Evaluation

  1. Convert KITTI ground truth poses into TUM format for monocular KITTI evaluation purpose.

    python src/kitti_poses_and_timestamps_to_trajectory.py \
    data/kitti/poses/07.txt \
    data/kitti/sequences/07/times.txt \
    results/kitti/07/pose.txt

    Note that we need TUM format here since trajectories from monocular SLAM on KITTI can only be saved in TUM format.

  2. Plot multiple trajectories with ground truth

    evo_traj tum {traj1}.txt {traj2}.txt --ref pose.txt -s --align_origin -p --plot_mode xz --save_plot plot_name

    Note that -as stands for --align --correct_scale

  3. Compute absolute pose error on trajectories

    evo_ape tum pose.txt {traj}.txt -as -p --plot_mode xz --save_plot plot_name

  4. Save plots

    evo_res results/*.zip -p --save_table results/table.csv

Dependencies

Acknowledgement

Team 21 of ROB 530 Mobile Robotics W23: Yulun Zhuang, Hao Liu, Xingqiao Zhu, Vaishnavi Harikumar and Janani Peri.