Paper Info
Title
Semi-Supervised Slam: Leveraging Low-Cost Sensors On Underground Autonomous Vehicles For Position Tracking
Abstract
This work presents Semi-Supervised SLAM - a method for developing a map suitable for coarse localization within an underground environment with minimal human intervention, with system characteristics driven by real-world requirements of major mining companies. This work leverages existing information common within a mining environment namely a surveyed mine map - which is used to sparsely ground map locations within the mine environment, increasing map accuracy and allowing localization within a global frame. Map creation utilizes a low cost camera sensor and minimal user information to produce a map which can be used for single camera localization within a mining environment. We evaluate the localization capabilities of the proposed approach in depth by performing data collection on operational underground mining vehicles within an active underground mine and by simulating occlusions common to the environment such as dust and water. The proposed system is capable of producing maps which have an average localization error 2.5 times smaller than the next best performing method ORB-SLAM2, comparable localization performance to a state-of-the-art deep learning approach (which is not a feasible solution due to both compute and training requirements) and is robust to simulated environmental obscurants.
Year
DOI
Venue
2018
10.1109/IROS.2018.8593750
2018 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS)
Field
DocType
ISSN
Data collection,Computer vision,Image sensor,Visual odometry,Computer science,User information,Ground,Artificial intelligence,Deep learning,Simultaneous localization and mapping,Underground mining (hard rock)
Conference
2153-0858
Citations 
PageRank 
References 
1
0.34
0
Authors
6
Name
Order
Citations
PageRank
Adam Jacobson1768.71
Zeng Fan241.65
David B. Smith334223.45
Nigel Boswell411.02
Thierry Peynot510714.82
Michael Milford6122184.09