Vision-Based Indoor Positioning of a Robotic Vehicle with a Floorplan

2018 ◽  
Author(s):  
John Noonan ◽  
Hector Rotstein ◽  
Amir Geva ◽  
Ehud Rivlin
Keyword(s):  
Indoor Positioning ◽  
Robotic Vehicle

scholarly journals Global Monocular Indoor Positioning of a Robotic Vehicle with a Floorplan

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 634 ◽  
Author(s):  
John Noonan ◽  
Hector Rotstein ◽  
Amir Geva ◽  
Ehud Rivlin
Keyword(s):  
Point Clouds ◽  
Indoor Positioning ◽  
Global Scale ◽  
Bundle Adjustment ◽  
Necessary Condition ◽  
Fusion Algorithm ◽  
Real World Data ◽  
Scale Invariant ◽  
Planar Structures ◽  
Robotic Vehicle

This paper presents a global monocular indoor positioning system for a robotic vehicle starting from a known pose. The proposed system does not depend on a dense 3D map, require prior environment exploration or installation, or rely on the scene remaining the same, photometrically or geometrically. The approach presents a new way of providing global positioning relying on the sparse knowledge of the building floorplan by utilizing special algorithms to resolve the unknown scale through wall–plane association. This Wall Plane Fusion algorithm presented finds correspondences between walls of the floorplan and planar structures present in the 3D point cloud. In order to extract planes from point clouds that contain scale ambiguity, the Scale Invariant Planar RANSAC (SIPR) algorithm was developed. The best wall–plane correspondence is used as an external constraint to a custom Bundle Adjustment optimization which refines the motion estimation solution and enforces a global scale solution. A necessary condition is that only one wall needs to be in view. The feasibility of using the algorithms is tested with synthetic and real-world data; extensive testing is performed in an indoor simulation environment using the Unreal Engine and Microsoft Airsim. The system performs consistently across all three types of data. The tests presented in this paper show that the standard deviation of the error did not exceed 6 cm.

Analysis of Image based Indoor Positioning Results in Complex Indoor Environment

2017 ◽  
Author(s):  
Juil Jeon ◽  
Juyoung Kim ◽  
Myoungin Ji ◽  
Youngsu Cho ◽  
Andrea Lingua ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Indoor Positioning

Alternate Trajectory Determination Using Multimode Automated Robotic Vehicle with Line Following and Object Following Mode

2018 ◽  
Vol 8 (3) ◽  
pp. 46
Author(s):  
Varun Kumar ◽  
Lakshya Gaur ◽  
Arvind Rehalia
Keyword(s):  
Ultrasonic Sensor ◽  
Robotic Assembly ◽  
Voltage Regulator ◽  
Automated Vehicle ◽  
Dc Motors ◽  
Arduino Uno ◽  
Robotic Vehicle ◽  
Line Following ◽  
Selection Of ◽  
Trajectory Determination

In this paper the authors have explained the development of robotic vehicle prepared by them, which operates autonomously and is not controlled by the users, except for selection of modes. The different modes of the automated vehicle are line following, object following and object avoidance with alternate trajectory determination. The complete robotic assembly is mounted on a chassis comprising of Arduino Uno, Servo motors, HC-SRO4 (Ultrasonic sensor), DC motors (Geared), L293D Motor Driver, IR proximity sensors, Voltage Regulator along with castor wheel and two normal wheels.

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