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lidar_bev

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A ROS package for LiDAR's Bird's Eye View projection.

File Explorer

Download Latest Version (.zip)

- - cloud_filter.hpp
- 16bv.launch
- 32bv.launch
- 64bv.launch
- offline_bev.launch
- birdview_detection_refiner.py
- detections_refinement.py
- draw_bev_detections.py
- max_points_map.py
- - cloud_filter.cpp
- lidar_bev.cpp
- offline_bev.cpp
.gitignore
CMakeLists.txt
package.xml
README.md

# Installation Guide

1. Get the code

git clone https://github.com/beltransen/lidar_bev

Downloads the entire project code from GitHub to your computer.

cd lidar_bev

Moves into the project folder you just downloaded.

2. Official Install Script

Easy Recommended

Prerequisites

Python 3 Python is required to use pip.

pip install progress numpy

Installs the package published on PyPI directly — no need to clone the source.

✅ After installing, open a new terminal and run the program's version command (e.g. --version) to confirm it worked.

Pulled directly from this repo's README.

3. CMake

Medium

Prerequisites

Git Needed to download the project code from GitHub.
CMake The tool used to generate build configuration.
C/C++ 컴파일러 Windows needs Visual Studio (Community edition, free), macOS needs Xcode Command Line Tools, Linux needs the gcc/g++ package.

mkdir build && cd build

Creates a folder to hold the build output and moves into it.

cmake ..

Analyzes the source code and generates build configuration files (must be run inside the build folder).

make

Compiles the code based on the generated build configuration to produce an executable.

✅ Check that an executable was created inside the build folder, then run it directly (e.g. ./build/app_name).

// repository documentation

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*lidar_bev* is a [ROS](https://www.ros.org/) package that computes the Bird's Eye View of a LiDAR pointcloud. It follows the encoding defined in [BirdNet](https://arxiv.org/abs/1805.01195). This representation is also used by the newer [BirdNet+](https://arxiv.org/abs/2003.04188).

This package was developed at the [Intelligent Systems Laboratory](http://www.uc3m.es/islab), Universidad Carlos III de Madrid, by Jorge Beltrán, Carlos Guindel, Francisco M. Moreno, Irene Cortés, and Daniel Cruzado.

### Where to start?
- If you want to generate BEV images for training and validation of models on the KITTI Object Benchmark, please run:
```
roslaunch lidar_bev offline_bev.launch
```

- For online operation, a ROS node is provided. Launch files for different LiDAR devices are located in `launch/`.
- The files in `scripts/` are mainly intended to perform the post-processing/refinement stage of the original BirdNet in order to obtain rotated boxes from the Faster R-CNN axis-aligned detections. This has been deprecated by BirdNet+.

### Run configuration

#### Bird's Eye View parameters
There are many parameters to configure the BEV. Default values lead to results published in BirdNet and BirdNet+. These are: *camera_fov*, *planes*, *h_res*, *v_res*, *low_opening*, *cell_size*, *grid_dim*, *grid_min_x*, *grid_max_x*, *grid_min_y*, *grid_max_y*, *max_height*, *min_height*, *num_slices*, *height_threshold*, *cell_size_height_map*, *grid_dim_height_map*, *get_ground*, *remove_floor*.

#### Offline mode
* *kitti_dir*: Path to `kitti/object` folder.
* *split_dir*: KITTI split set. Can be *training* or *testing*.
* *saving_path*: folder name (or full path) to save resulting images. If no full path is provided, the folder will be created in `$HOME/.ros/`

#### Online mode (ROS node)
* *lidar_tf_frame*: name of the LiDAR frame
* *camera_tf_frame*: name of the camera frame

##### Inputs (Subscribed Topics)
* *cloud_topic* ([sensor_msgs::PointCloud2](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/PointCloud2.html)): LiDAR pointcloud

##### Outputs (Published Topics)
* *bird_view* ([sensor_msgs/Image](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/Image.html)): BEV image
* *bird_ground* ([sensor_msgs/Image](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/Image.html)): Image representing the ground estimation
* *ground_cloud* ([sensor_msgs::PointCloud2](http://docs.ros.org/melodic/api/sensor_msgs/html/msg/PointCloud2.html)): Pointcloud to visualize the ground estimation in Rviz.

### Dependencies

This repository depends on [ROS](http://ros.org/). ROS *Kinetic* and newer versions should work. Additionally, an auxiliary script is used to compute the density channel of the BEV (*max_points_map.py*). To setup the dependencies, please run:
```
pip install progress numpy
```
##### ROS Dependencies
* [cv_bridge](http://wiki.ros.org/cv_bridge)
* [image_transport](http://wiki.ros.org/image_transport)
* [pcl_ros](http://wiki.ros.org/pcl_ros)
* [sensor_msgs](http://wiki.ros.org/sensor_msgs)
* [tf](http://wiki.ros.org/tf)

### Citation

If you find this module useful, consider citing one of our works:

##### The original BirdNet, where this BEV encoding was introduced.
```
@inproceedings{Beltran2018,
    title = {{BirdNet: a 3D Object Detection Framework from LiDAR information}},
    author = {Beltrán, Jorge and Guindel, Carlos and Moreno, Francisco Miguel and Cruzado, Daniel and García, Fernando and de la Escalera, Arturo},
    booktitle = {Proc. IEEE International Conference on Intelligent Transportation Systems (ITSC)},
    pages = {3517--3523},
    publisher = {IEEE},
    year = {2018}
}
```

##### The next generation, BirdNet+.
```
@misc{Barrera2020,
    title = {{BirdNet+: End-to-End 3D Object Detection in LiDAR Bird's Eye View}},
    author = {Barrera, Alejandro and Guindel, Carlos and Beltrán, Jorge and García, Fernando},
    booktitle = {arXiv:2003.04188 [cs.CV]},
    url = {http://arxiv.org/abs/2003.04188},
    year = {2020}
}
```