Overcoming the perceptual limitations of humanoid robots requires representations exploitable by highly integrable simulation, sensing, planning and acting components. Therefore, a novel active visual localization component for humanoid robots based on particle filtering in CAD environments is introduced. Specifically, two new components are presented: i) A vector-graphics prediction method employing hierarchical CAD environmental representations is presented. ii) A gaze attention method within the prediction-update cycle of the particle filter increases the available amount of visual features for localization while allowing adjustable task coupling. Finally, large and unobstructive ground-truth validation with the humanoid robot ARMAR-IIIb [1] in a made-for-humans environment shows the robustness, accuracy and performance of the proposed methods.

Humanoid robots should be able to visually recognize objects and estimate their 6D pose in real environmental conditions with their limited sensor capabilities. In order to achieve these visual skills, it is necessary to establish an optimal visual transducer connecting the scene layout with the internal representations of objects and places. This visual transducer should capture the noiseless visual manifold of the scene with high-dynamic-range in an efficient manner. Our endeavor is to develop such a visual transducer using the widespread LDR cameras in humanoid robots. In our previous work, the noiseless acquisition of continuous images [1] and the improved radio-metric calibration [2] already enabled the humanoid robots to attain the desired visual manifold in terms of quality. However, since the radiance range of the scene can be very wide, the required amount of exposures to capture the visual manifold (robustly without radiance inconsistencies) turns impractically large in terms of scope, granularity and acquisition time. In this article, a method for estimating the minimal amount of exposures and their particular integration times is presented. This method integrates our previous work in order to synthesize HDR images with the minimal amount of exposures while ensuring the high quality of the resulting image. Conclusively, the minimal exposure set provides performance improvements without quality trade-off. Experimental evaluation is presented with the humanoid robots ARMAR-III a, b [3].

Humanoid robots should be able to grasp and handle objects in the environment, even if the objects are seen for the first time. A plausible solution to this problem is to categorize these objects into existing classes with associated actions and functional knowledge. So far, efforts on visual object categorization using humanoid robots have either been focused on appearance-based methods or have been restricted to object recognition without generalization capabilities. In this work, a shape model-based approach using stereo vision and machine learning for object categorization is introduced. The state-of-the-art features for shape matching and shape retrieval were evaluated and selectively transfered into the visual categorization. Visual sensing from different vantage points allows the reconstruction of 3D mesh models of the objects found in the scene by exploiting knowledge about the environment for model-based segmentation and registration. These reconstructed 3D mesh models were used for shape feature extraction for categorization and provide sufficient information for grasping and manipulation. Finally, the visual categorization was successfully performed with a variety of features and classifiers allowing proper categorization of unknown objects even when object appearance and shape substantially differ from the training set. Experimental evaluation with the humanoid robot ARMAR-IIIa is presented.

In this paper, a geometric approach for global self-localization based on a world-model and active stereo vision is introduced. The method uses class specific object recognition algorithms to obtain the location of entities within the surroundings. The perceived entities in recognition trials are simultaneously filtered and fused to provide a robust set of class features. These classified perceptions which simultaneously satisfy geometric and topological constraints are employed for pruning purposes upon the world-model generating the location hypotheses set. Finally, the hypotheses are validated and disambiguated by applying visual recognition algorithms to selected entities of the world-model. The proposed approach has been successfully used with a humanoid robot.

In this paper we introduce a conformal model for stereoscopic perception systems. This model fuses stereoscopic views using an extended 3D horopter concept. This model is suitable for building a powerful binocular head for humanoids, intuitive man-machine interfaces and advanced 3D automated visual inspection

In this paper we propose a conformal model for human like vision. In our model the two views are fused in an extended 3D horopter concept. The inverse transformation of the horopter spheres and Poncelet points leads remarkably to a 3D log-polar representation of the visual space. This representation is quite effective for processing the visual space information. We believe that this model is very suitable for building a powerful binocular head for humanoids.