Quick Start within 3 Minutes

Compiling tests on ubuntu 16.04, 18.04 and 20.04 with ros installed all passed. You just execute the commands one by one.

sudo apt-get install libarmadillo-dev
git clone https://github.com/bigsuperZZZX/ego_planner.git
cd ego-planner
catkin_make
source devel/setup.bash
roslaunch ego_planner simple_run.launch

If your link to github is slow, I recommend you to try the gitee repository https://gitee.com/iszhouxin/ego_planner. They synchronize automatically.

Acknowledgements

The framework of this repository is based on Fast-Planner by Zhou Boyu who achieves impressive proformance on quaorotor local planning.

The L-BFGS solver we use is modified by wangzhepei@zju.edu.cn from xxx. It is a C++ head-only single file, which is lightweight and easy to use.

The map generated in simulation is from mockamap by Willim Wu.

The hardware architecture is based on an open source implemation from Teach-Repeat-Replan.

EGO-Planner

EGO-Planner: An ESDF-free Gradient-based Local Planner for Quadrotors

EGO-Planner is a lightweight gradient-based local planner without ESDF construction, which significantly reduces computation time (around 1ms) compared to some state-of-the-art methods .

Video Links: YouTube, bilibili (for Mainland China)

1. Standard Compilation

Requirements: ubuntu 16.04, 18.04 or 20.04 with ros-desktop-full installation.

Step 1: install Armadillo, which is required by uav_simulator.

sudo apt-get install libarmadillo-dev

Step 2: clone the code from github or gitee (for Mainland China). This two repositories synchronize automaticly.

From github

git clone https://github.com/bigsuperZZZX/ego-planner.git

Or from gitee

git clone https://gitee.com/iszhouxin/ego_planner.git

Step 3: compile.

cd ego-planner
catkin_make -DCMAKE_BUILD_TYPE=Release

Step 4: run.

In a terminal at the ego-planner folder, open the rviz for visuallization and interactions

source devel/setup.bash
roslaunch ego-planner rviz.launch

In another terminal at the same folder, run the simulation and planner by

source devel/setup.bash
roslaunch ego-planner run_in_sim.launch

Then you can follow the gif below to select you targets.

2. Using an IDE

We recommend using vscode, the project file has been included in the code you cloned, which is the .vscode folder. This folder is hidden by default.

First, re-compile the code using command

catkin_make -DCMAKE_BUILD_TYPE=Release -DCMAKE_EXPORT_COMPILE_COMMANDS=Yes

It will export a compile commands file, which can help vscode to determine the code architecture.

Then launch vscode and select the ego-planner folder to open.

code ~/<......>/ego-planner/

Press Ctrl+Shift+B to compile the code in vscode. This command is defined in .vscode/tasks.json.

Then close and restart vscode, you will see the vscode has already known the code architecture and can perform auto completion & jump.

3.Use GPU or Not

Packages in this repo, local_sensing have GPU, CPU two different versions. By default, they are in CPU version for better compatibility. By changing

set(ENABLE_CUDA false)

in the CMakeList.txt in local_sensing packages, to

set(ENABLE_CUDA true)

CUDA will be turned-on to generate depth images as a real depth camera does.

Please remember to also change the 'arch' and 'code' flags in the line of

    set(CUDA_NVCC_FLAGS 
      -gencode arch=compute_61,code=sm_61;
    ) 

in 'CMakeList', if you encounter compiling error due to different Nvidia graphics card you use. You can check the right code here.

Don't forget to re-compile the code!

local_sensing is the simulated sensors. If ENABLE_CUDA true, it mimics the depth measured by stereo cameras and renders a depth image by GPU. If ENABLE_CUDA false, it will publish pointclouds with no ray-casting. Our local mapping module automatically selects whether depth images or pointclouds as its input.

For installation of CUDA, please go to CUDA ToolKit

4.Utilize the Full Performance of CPU

The computation time of our planner is too short to let the OS to increase CPU frequency, which makes the computation time tend to be longer and unstable.

Therefore, we recommend that you manually set the CPU frequency to the maximum. Install this tool by

sudo apt install cpufrequtils

Then you can set the CPU frequency to the maximum allowed by

sudo cpufreq-set -g performance

More information can be found in http://www.thinkwiki.org/wiki/How_to_use_cpufrequtils.

Note that CPU frequency may still decrease due to high temperature in high load.

6. Licence

The source code is released under GPLv3 license.

7. Maintaince

We are still working on extending the proposed system and improving code reliability.

For any technical issues, please contact Xin Zhou (iszhouxin@zju.edu.cn) or Fei GAO (fgaoaa@zju.edu.cn).

For commercial inquiries, please contact Fei GAO (fgaoaa@zju.edu.cn).