Imperial College London
Alternate

DrStefanLeutenegger

Faculty of Engineering, Department of Computing

Visiting Reader
 
 
 
//

Contact

 

s.leutenegger Website

 
 
//

Location

 

ACE ExtensionSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@unpublished{Dai:2020,
author = {Dai, A and Papatheodorou, S and Funk, N and Tzoumanikas, D and Leutenegger, S},
publisher = {arXiv},
title = {Fast frontier-based information-driven autonomous exploration with an MAV},
url = {http://arxiv.org/abs/2002.04440v2},
year = {2020}
}
Download

RIS format (EndNote, RefMan)

TY  - UNPB
AB  - Exploration and collision-free navigation through an unknown environment is afundamental task for autonomous robots. In this paper, a novel explorationstrategy for Micro Aerial Vehicles (MAVs) is presented. The goal of theexploration strategy is the reduction of map entropy regarding occupancyprobabilities, which is reflected in a utility function to be maximised. Weachieve fast and efficient exploration performance with tight integrationbetween our octree-based occupancy mapping approach, frontier extraction, andmotion planning-as a hybrid between frontier-based and sampling-basedexploration methods. The computationally expensive frontier clustering employedin classic frontier-based exploration is avoided by exploiting the implicitgrouping of frontier voxels in the underlying octree map representation.Candidate next-views are sampled from the map frontiers and are evaluated usinga utility function combining map entropy and travel time, where the former iscomputed efficiently using sparse raycasting. These optimisations along withthe targeted exploration of frontier-based methods result in a fast andcomputationally efficient exploration planner. The proposed method is evaluatedusing both simulated and real-world experiments, demonstrating clear advantagesover state-of-the-art approaches.
AU  - Dai,A
AU  - Papatheodorou,S
AU  - Funk,N
AU  - Tzoumanikas,D
AU  - Leutenegger,S
PB  - arXiv
PY  - 2020///
TI  - Fast frontier-based information-driven autonomous exploration with an MAV
UR  - http://arxiv.org/abs/2002.04440v2
UR  - http://hdl.handle.net/10044/1/79816
ER  -
Download