Title
Mapping, Planning, and Sample Detection Strategies for Autonomous Exploration.
Abstract
This paper presents algorithmic advances and field trial results for autonomous exploration and proposes a solution to perform simultaneous localization and mapping (SLAM), complete coverage, and object detection without relying on GPS or magnetometer data. We demonstrate an integrated approach to the exploration problem, and we make specific contributions in terms of mapping, planning, and sample detection strategies that run in real-time on our custom platform. Field tests demonstrate reliable performance for each of these three main components of the system individually, and high-fidelity simulation based on recorded data playback demonstrates the viability of the complete solution as applied to the 2013 NASA Sample Return Robot Challenge.
Year
DOI
Venue
2014
10.1002/rob.21490
JOURNAL OF FIELD ROBOTICS
Field
DocType
Volume
Computer vision,Object detection,Simulation,Exploration problem,Real-time computing,Global Positioning System,Artificial intelligence,Engineering,Simultaneous localization and mapping,Robot
Journal
31.0
Issue
ISSN
Citations 
SP1.0
1556-4959
12
PageRank 
References 
Authors
0.54
52
9
Name
Order
Citations
PageRank
Arun Das1495.98
Michael Diu2261.60
Neil Mathew3552.67
Christian Scharfenberger413810.61
James Servos5302.22
Andy Wong6211.56
John S. Zelek723333.55
David A. Clausi8108289.57
Steven Lake Waslander944346.89