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Relative visual localization (RVL) for UAV navigation
Author(s): Andy Couturier; Moulay A. Akhloufi
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Paper Abstract

Most of today's UAVs make use of multi-sensor GNSS/INS fusion for localization during navigation. In such a context GNSS systems are used as a compact and cost-effective way to constrain the unbounded error induced by the INS sensors on the localization. Unfortunately, GNSS systems have been proven to be unreliable in multiple contexts. The drawback of such an approach resides in the radio communications necessary to acquire the localization data. Radio communication systems are prone to availability problems in some environments, to signal alteration and to interference. The root cause of the problem resides in the use of global information to solve a local problem. In this work, we propose the use of local visual information to perform relative localization in an unknown outdoor environment. The algorithm uses feature point methods to extract salient points from a set of images pertaining to possible matches during the navigation. The extracted features are matched with available visual data stored during previous navigation or from an aerial view map. Different feature extraction techniques were analyzed, and ORB was the one that gave the best mean absolute error. The estimated distance between the best match and ground-truth localization was within 70 meters on average at an altitude of 150 meters. Experimental tests were conducted on outdoor videos captured using a quadcopter. The obtained results are promising and show the possibility of using relative visual data in GPS/GNSS-denied environments to improve the robustness of UAVs navigation.

Paper Details

Date Published: 2 May 2018
PDF: 14 pages
Proc. SPIE 10642, Degraded Environments: Sensing, Processing, and Display 2018, 106420O (2 May 2018); doi: 10.1117/12.2304901
Show Author Affiliations
Andy Couturier, Univ. de Moncton (Canada)
Moulay A. Akhloufi, Univ. de Moncton (Canada)

Published in SPIE Proceedings Vol. 10642:
Degraded Environments: Sensing, Processing, and Display 2018
John (Jack) N. Sanders-Reed; Jarvis (Trey) J. Arthur, Editor(s)

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