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2016 | OriginalPaper | Chapter

Vision-Based Self-contained Target Following Robot Using Bayesian Data Fusion

Authors : Andrés Echeverri Guevara, Anthony Hoak, Juan Tapiero Bernal, Henry Medeiros

Published in: Advances in Visual Computing

Publisher: Springer International Publishing

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Abstract

Several visual following robots have been proposed in recent years. However, many require the use of several, expensive sensors and often the majority of the image processing and other calculations are performed off-board. This paper proposes a simple and cost effective, yet robust visual following robot capable of tracking a general object with limited restrictions on target characteristics. To detect the objects, tracking-learning-detection (TLD) is used within a Bayesian framework to filter and fuse the measurements. A time-of-flight (ToF) depth camera is used to refine the distance estimates at short ranges. The algorithms are executed in real-time (approximately 30 fps) in a Jetson TK1 embedded computer. Experiments were conducted with different target objects to validate the system in scenarios including occlusions and various illumination conditions as well as to show how the data fusion between TLD and the ToF camera improves the distance estimation.

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previous chapter Image Fusion Quality Measure Based on a Multi-scale Approach

next chapter Dual Back-to-Back Kinects for 3-D Reconstruction

http://www.nvidia.com/object/tegra-k1-processor.html.

Although other ToF cameras such as the classic SR4000 from MESA imaging, the PMD CamCube 3.0 or SoftKinetic’s DS536A have ranges of up to 5 m, the low-cost and lightweight Senz3D was deemed sufficient for our purposes.

Note that the set points \(s_{p_u}\) and \(s_{p_z}\) correspond to the desired target position with respect to the robot, not to the actual robot position. The controllers use the set points to move the robot so that the difference between the estimated position and the set point is minimized.

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Title: Vision-Based Self-contained Target Following Robot Using Bayesian Data Fusion
Authors: Andrés Echeverri Guevara
Anthony Hoak
Juan Tapiero Bernal
Henry Medeiros
Publisher: Springer International Publishing
Book: Advances in Visual Computing
Print ISBN: 978-3-319-50834-4

Electronic ISBN: 978-3-319-50835-1

Copyright Year: 2016
DOI: https://doi.org/10.1007/978-3-319-50835-1_76

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