Livox Detection-simu V1.0: Trained on Simulated Data, Tested in the Real World [Livox Simu-dataset]

Introduction

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.

Demo

Dependencies

• python3.6+
• tensorflow1.13+ (tested on 1.13.0)
• pybind11
• ros

Installation

1. Clone this repository.

2. Clone pybind11 from pybind11.

   $ cd utils/lib_cpp
   $ git clone https://github.com/pybind/pybind11.git

3. Compile C++ module in utils/lib_cpp by running the following command.

   $ mkdir build && cd build
   $ cmake -DCMAKE_BUILD_TYPE=Release ..
   $ make

4. copy the lib_cpp.so to root directory:

   $ cp lib_cpp.so ../../../

5. Download the pre_trained model and unzip it to the root directory.

Run

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 /livox/lidar.

Support

You can get support from Livox with the following methods :

• Send email to cs@livoxtech.com with a clear description of your problem and your setup
• Report issue on github