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Article

FloorVLoc: A Modular Approach to Floorplan Monocular Localization

1
Intelligent Systems Lab, Department of Computer Science, Technion—Israel Institute of Technology, Haifa 3200003, Israel
2
Rafael Advanced Defense Systems Ltd., Department of Electrical Engineering, Technion—Israel Institute of Technology, Haifa 3200003, Israel
*
Author to whom correspondence should be addressed.
Robotics 2020, 9(3), 69; https://doi.org/10.3390/robotics9030069
Received: 14 August 2020 / Revised: 7 September 2020 / Accepted: 8 September 2020 / Published: 10 September 2020
(This article belongs to the Special Issue Feature Papers 2020)
Intelligent vehicles for search and rescue, whose mission is assisting emergency personnel by visually exploring an unfamiliar building, require accurate localization. With GPS not available, and approaches relying on new infrastructure installation, artificial landmarks, or pre-constructed dense 3D maps not feasible, the question is whether there is an approach which can combine ubiquitous prior map information with a monocular camera for accurate positioning. Enter FloorVLoc—Floorplan Vision Vehicle Localization. We provide a means to integrate a monocular camera with a floorplan in a unified and modular fashion so that any monocular visual Simultaneous Localization and Mapping (SLAM) system can be seamlessly incorporated for global positioning. Using a floorplan is especially beneficial since walls are geometrically stable, the memory footprint is low, and prior map information is kept at a minimum. Furthermore, our theoretical analysis of the visual features associated with the walls shows how drift is corrected. To see this approach in action, we developed two full global positioning systems based on the core methodology introduced, operating in both Monte Carlo Localization and linear optimization frameworks. Experimental evaluation of the systems in simulation and a challenging real-world environment demonstrates that FloorVLoc performs with an average error of 0.06 m across 80 m in real-time. View Full-Text
Keywords: indoor positioning; mobile robotics; visual SLAM; search and rescue indoor positioning; mobile robotics; visual SLAM; search and rescue
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MDPI and ACS Style

Noonan, J.; Rivlin, E.; Rotstein, H. FloorVLoc: A Modular Approach to Floorplan Monocular Localization. Robotics 2020, 9, 69. https://doi.org/10.3390/robotics9030069

AMA Style

Noonan J, Rivlin E, Rotstein H. FloorVLoc: A Modular Approach to Floorplan Monocular Localization. Robotics. 2020; 9(3):69. https://doi.org/10.3390/robotics9030069

Chicago/Turabian Style

Noonan, John, Ehud Rivlin, and Hector Rotstein. 2020. "FloorVLoc: A Modular Approach to Floorplan Monocular Localization" Robotics 9, no. 3: 69. https://doi.org/10.3390/robotics9030069

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