OTD (Observation Time Difference) an online dynamic traces removal package. It takes voxels as the smallest unit for dynamic traces removal, and is based on the assumption that static voxels always appear and disappear simultaneously with the ground below them. Therefore, the voxel that appears later than the ground as suddenly appear dynamic voxel, and voxel that disappears earlier than the ground as suddenly disappear dynamic voxel. We call this method of judging dynamic voxels as observation time difference.
Ubuntu >= 18.04
For Ubuntu 18.04 or higher, the default PCL and Eigen is enough for OTD to work normally.
ROS >= Melodic. ROS Installation
PCL >= 1.8, Follow PCL Installation.
Eigen >= 3.3.4, Follow Eigen Installation.
OTD needs boost to load KITTI dataset.
Clone the repository and catkin_make:
cd ~/$A_ROS_DIR$/src
git clone git@github.com:RongguangWu/OTD.git
catkin_make
source devel/setup.bash
- Download the KITTI dataset from the SemanticKITTI official website (http://www.semantic-kitti.org/index.html).
- Modify the dataset_path, start_frame, and end_frame parameters in the yaml file under the config folder.
- Run
cd ~/$A_ROS_DIR$
source devel/setup.bash
roslaunch otd otd_kitti.launch
Your bag file needs to contain the LiDAR point cloud topic (sensor_msgs/PointCloud2) and the odometry topic (nav_msgs/Odometry). The point cloud is in the local coordinate frame, while the odometry is the transformation from the local frame to the global frame.
Special note: OTD requires ground segmentation, and the ground segmentation method we use requires that the height of the LiDAR from the ground is fixed. Therefore, for handheld or airborne devices, our method will not work.
- Modify the lid_topic, odom_topic, and sensor_height(The height of the origin of the local coordinate frame from the ground) parameters in the online.yaml file under the config folder.
- Run
cd ~/$A_ROS_DIR$
source devel/setup.bash
roslaunch otd otd_online.launch
Thanks for the iVox from Faster-LIO(C. Bai, T. Xiao, Y. Chen, H. Wang, F. Zhang, and X. Gao. Faster-lio: Lightweight tightly coupled lidar-inertial odometry using parallel sparse incremental voxels).