This repository contains the code for a shadowcasting-based next-best-view planner, presented in the paper:
A Shadowcasting-Based Next-Best_View Planner for Autonomous 3D Exploration
Ana Batinovic, Antun Ivanovic, Tamara Petrovic and Stjepan Bogdan.
If you use this code in your research, please cite our journal paper:
@ARTICLE{Batinovic-RAL-2022,
author={Batinovic, Ana and Ivanovic, Antun and Petrovic, Tamara and Bogdan, Stjepan},
journal={IEEE Robotics and Automation Letters},
title={A Shadowcasting-Based Next-Best-View Planner for Autonomous 3D Exploration},
year={2022},
volume={7},
number={2},
pages={2969-2976},
doi={10.1109/LRA.2022.3146586}}
The repository is based on the proposed nbvplanner.
Proposed shadowcasting-based next-best-view planner is capable of autonomously exploring a previously unknown bounded area and creating an OctoMap of the environment. The results showed an improved behaviour in terms of both computation and total exploration time compared to state-of-the-art strategies. The proposed information gain calculation and path evaluation ensures target evaluation in a short computation time, while a novel dead end recovery algorithm speeds up the exploration process. This 3D exploration planner has been successfully tested and analysed in simulation scenarios and compared with state-of-the-are strategies.
Video recordings of shadowcasting-based next-best-view exploration can be found at YouTube.
This README gives a brief overview. For more information, please refer to the wiki, where all further instructions on installation, visualization of exploration progress, as well as demo scenarios can be found.
You can contact Ana Batinovic for any question or remark.