Livox Detection-simu is a robust and real-time detection package trained on Livox Simu-dataset. It only uses 14k frames of simulated data for training, and performs effective detection in the real world. The inference time is about 50ms on 2080Ti for 200m*100m range detection.
We hope this project can help you make better use of Livox Simu-dataset. In order to improve the performance of the detector, data augmentation such as object insertion and background mix-up is necessary.
tensorflow1.13+(tested on 1.13.0)
- Clone this repository.
$ cd utils/lib_cpp $ git clone https://github.com/pybind/pybind11.git
- Compile C++ module in utils/lib_cpp by running the following command.
$ mkdir build && cd build $ cmake -DCMAKE_BUILD_TYPE=Release .. $ make
- copy the
lib_cpp.soto root directory:
$ cp lib_cpp.so ../../../
- Download the pre_trained model and unzip it to the root directory.
For sequence frame detection
Download the provided rosbags : rosbag and then
$ roscore $ rviz -d ./config/show.rviz $ python livox_detection_simu.py $ rosbag play *.bag -r 0.1
The network inference time is around
25ms, but the point cloud data preprocessing module takes a lot of time based on python. If you want to get a faster real time detection demo, you can modify the point cloud data preprocessing module with c++.
To play with your own rosbag, please change your rosbag topic to
You can get support from Livox with the following methods :
- Send email to [email protected] with a clear description of your problem and your setup
- Report issue on github