Fiesta

Incremental ESDF Map for motion planning

Euclidean Signed Distance Field (ESDF) is useful for online motion planning of aerial robots since it can easily query the distance_ and gradient information against obstacles. Fast incrementally built ESDF map is the bottleneck for conducting real-time motion planning. In this paper, we investigate this problem and propose a mapping system called Fiesta to build global ESDF map incrementally. By introducing two independent updating queues for inserting and deleting obstacles separately, and using Indexing Data Structures and Doubly Linked Lists for map maintenance, our algorithm updates as few as possible nodes using a BFS framework. Our ESDF map has high computational performance and produces near-optimal results. We show our method outperforms other up-to-date methods in term of performance and accuracy by both theory and experiments. We integrate Fiesta into a completed quadrotor system and validate it by both simulation and onboard experiments. We release our method as open-source software for the community.

The paper of this method is submitted to the 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019), under review. The draft is shown on arxiv here.

Installation

cd ~/catkin_ws/src
git clone https://github.com/hlx1996/Fiesta.git
cd ../
catkin_make
source ~/catkin_ws/devel/setup.bash

Usage example

roslaunch Fiesta demo.launch

A few motivating and useful examples of how your product can be used. Spice this up with code blocks and potentially more screenshots.

For more examples and usage, please refer to the Wiki.

Release History

Prediction: 1.0.0 will be our first elegant version.

• 0.6.0
  • CHANGE: Refactor code according Google C++ Code Style
• 0.5.3
  • CHANGE: Improve the performance of the depth conversion process
• 0.5.2
  • ADD: Support local map
  • ADD: Support raw depth image as input, and depth filter of consistency
• 0.5.1
  • CHANGE: Improve performance of raycasting a lot
• 0.5.0
  • ADD: Give Local / global update / visualization options
  • ADD: Show Performance visualization directly on rviz
  • CHANGE: Deprecate older visualization, will be deleted in next release
• 0.4.1
  • CHANGE: Give better Visualization based on a slice of ESDF and pointcloud of obstacles
• 0.4.0
  • ADD: Support LIDAR Input
  • ADD: Support Deterministic Occupancy Grid Map
• 0.3.2
  • ADD: Implement Multi-thread Raycasting for array implementation
• 0.3.1
  • ADD: Implement Hash table with blocks
• 0.3.0
  • ADD: Implement Hash table
• 0.2.0
  • ADD: Support Raycasting from point cloud_
  • ADD: Support Probabilistic Occupancy Grid Map Fusion
  • CHANGE: Patch code for limited observations
• 0.1.0
  • The first proper release, support ESDF for fully dynamic case and implemented in array
• 0.0.1
  • Support ESDF for insert-only case

Meta

hlx1996 – [email protected]

Distributed under the MIT license. See LICENSE for more information.

Contributing

1. Fork it (https://github.com/hlx1996/Fiesta/fork)
2. Create your feature branch (git checkout -b feature/fooBar)
3. Commit your changes (git commit -am 'Add some fooBar')
4. Push to the branch (git push origin feature/fooBar)
5. Create a new Pull Request