/cube_slam

CubeSLAM: Monocular 3D Object Detection and SLAM

Primary LanguageC++OtherNOASSERTION

Cube SLAM

This code contains two mode:

  1. object SLAM integrated with ORB SLAM. See orb_object_slam Online SLAM with ros bag input. It reads the offline detected 3D object.
  2. Basic implementation for Cube only SLAM. See object_slam Given RGB and 2D object detection, the algorithm detects 3D cuboids from each frame then formulate an object SLAM to optimize both camera pose and cuboid poses. is main package. detect_3d_cuboid is the C++ version of single image cuboid detection, corresponding to a matlab version.

Authors: Shichao Yang

Related Paper:

  • CubeSLAM: Monocular 3D Object SLAM, IEEE Transactions on Robotics 2019, S. Yang, S. Scherer PDF

If you use the code in your research work, please cite the above paper. Feel free to contact the authors if you have any further questions.

Installation

Prerequisites

This code contains several ros packages. We test it in ROS indigo/kinetic, Ubuntu 14.04/16.04, Opencv 2/3. Create or use existing a ros workspace.

mkdir -p ~/cubeslam_ws/src
cd ~/cubeslam_ws/src
catkin_init_workspace
git clone git@github.com:shichaoy/cube_slam.git
cd cube_slam

Compile dependency g2o

sh install_dependenices.sh

Compile

cd ~/cubeslam_ws
catkin_make -j4

Running

source devel/setup.bash
roslaunch object_slam object_slam_example.launch

You will see results in Rviz. Default rviz file is for ros indigo. A kinetic version is also provided.

To run orb-object SLAM in folder orb_object_slam, download data. See correct path in mono.launch, then run following in two terminal:

roslaunch orb_object_slam mono.launch
rosbag play mono.bag --clock -r 0.5

To run dynamic orb-object SLAM mentioned in the paper, download data. Similar to above, set correct path in mono_dynamic.launch, then run the launch file with bag file.

If compiling problems met, please refer to ORB_SLAM.

Notes

  1. For the online orb object SLAM, we simply read the offline detected 3D object txt in each image. Many other deep learning based 3D detection can also be used similarly especially in KITTI data.

  2. In the launch file (object_slam_example.launch), if online_detect_mode=false, it requires the matlab saved cuboid images, cuboid pose txts and camera pose txts. if true, it reads the 2D object bounding box txt then online detects 3D cuboids poses using C++.

  3. object_slam/data/ contains all the preprocessing data. depth_imgs/ is just for visualization. pred_3d_obj_overview/ is the offline matlab cuboid detection images. detect_cuboids_saved.txt is the offline cuboid poses in local ground frame, in the format "3D position, 1D yaw, 3D scale, score". pop_cam_poses_saved.txt is the camera poses to generate offline cuboids (camera x/y/yaw = 0, truth camera roll/pitch/height) truth_cam_poses.txt is mainly used for visulization and comparison.

    filter_2d_obj_txts/ is the 2D object bounding box txt. We use Yolo to detect 2D objects. Other similar methods can also be used. preprocessing/2D_object_detect is our prediction code to save images and txts. Sometimes there might be overlapping box of the same object instance. We need to filter and clean some detections. See the filter_match_2d_boxes.m in our matlab detection package.