Abstract
The principal research objective of this book is to discuss, understand and implement the essential environmental vision-model coupling for humanoid robots. Concretely, the coupling of model-based visual environmental object recognition with model-based visual global self-localization and its interrelated data handling are the ultimate goals to be achieved. These skills require novel methods to overcome the current sensing and matching limitations. The proposed algorithms and their efficient implementation in a platform independent software system have enabled humanoid robots to attain the bidirectional vision-model coupling. The developed methods apply active stereo vision and use geometric CAD models of real made-for-humans environments. The core applications are tasks involving self-localization and object recognition for grasping and manipulation.
A journey of a thousand miles begins with a single step
Lao Tzu
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Notes
- 1.
The use of multiple heterogeneous input and output channels in a system.
- 2.
The pose of an object in 3D space is uniquely defined by a 3D vector location and three orientation angles.
- 3.
A lambertian surface is characterized by its isotropic luminance. This restriction is partially relaxed by metal and glass elements in the environment (see Chap. 5).
- 4.
The collaborative research center 588 “Humanoid Robots - Learning and Cooperating Multimodal Robots” founded by the Deutsche Forschungs-Gemeinschaft DFG.
- 5.
Off-the-shelf cameras providing range-quantized (8-bit), low-dynamic-range (LDR) based on color filter array CFA (Bayer pattern) sensors, such as the IEEE-1394a DragonFly cameras [9] in the humanoid robots ARMAR-IIIa and ARMAR-IIIb.
- 6.
Due to the raw images nature, the density property of real numbers is not hold.
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González Aguirre, D.I. (2019). Introduction. In: Visual Perception for Humanoid Robots. Cognitive Systems Monographs, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-319-97841-3_1
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