4,074 research outputs found
A 3D descriptor to detect task-oriented grasping points in clothing
© 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Manipulating textile objects with a robot is a challenging task, especially because the garment perception is difficult due to the endless configurations it can adopt, coupled with a large variety of colors and designs. Most current approaches follow a multiple re-grasp strategy, in which clothes are sequentially grasped from different points until one of them yields a recognizable configuration. In this work we propose a method that combines 3D and appearance information to directly select a suitable grasping point for the task at hand, which in our case consists of hanging a shirt or a polo shirt from a hook. Our method follows a coarse-to-fine approach in which, first, the collar of the garment is detected and, next, a grasping point on the lapel is chosen using a novel 3D descriptor.
In contrast to current 3D descriptors, ours can run in real time, even when it needs to be densely computed over the input image. Our central idea is to take advantage of the structured nature of range images that most depth sensors provide and, by exploiting integral imaging, achieve speed-ups of two orders of magnitude with respect to competing approaches, while maintaining performance. This makes it especially adequate for robotic applications as we thoroughly demonstrate in the experimental section.Peer ReviewedPostprint (author's final draft
Fast, Accurate and Object Boundary-Aware Surface Normal Estimation from Depth Maps
This paper proposes a fast and accurate surface normal estimation method
which can be directly used on depth maps (organized point clouds). The surface
normal estimation process is formulated as a closed-form expression. In order
to reduce the effect of measurement noise, the averaging operation is utilized
in multi-direction manner. The multi-direction normal estimation process is
reformulated in the next step to be implemented efficiently. Finally, a simple
yet effective method is proposed to remove erroneous normal estimation at depth
discontinuities. The proposed method is compared to well-known surface normal
estimation algorithms. The results show that the proposed algorithm not only
outperforms the baseline algorithms in term of accuracy, but also is fast
enough to be used in real-time applications
Efficient 3D Segmentation, Registration and Mapping for Mobile Robots
Sometimes simple is better! For certain situations and tasks, simple but robust methods can achieve the same or better results in the same or less time than related sophisticated approaches. In the context of robots operating in real-world environments, key challenges are perceiving objects of interest and obstacles as well as building maps of the environment and localizing therein. The goal of this thesis is to carefully analyze such problem formulations, to deduce valid assumptions and simplifications, and to develop simple solutions that are both robust and fast. All approaches make use of sensors capturing 3D information, such as consumer RGBD cameras. Comparative evaluations show the performance of the developed approaches. For identifying objects and regions of interest in manipulation tasks, a real-time object segmentation pipeline is proposed. It exploits several common assumptions of manipulation tasks such as objects being on horizontal support surfaces (and well separated). It achieves real-time performance by using particularly efficient approximations in the individual processing steps, subsampling the input data where possible, and processing only relevant subsets of the data. The resulting pipeline segments 3D input data with up to 30Hz. In order to obtain complete segmentations of the 3D input data, a second pipeline is proposed that approximates the sampled surface, smooths the underlying data, and segments the smoothed surface into coherent regions belonging to the same geometric primitive. It uses different primitive models and can reliably segment input data into planes, cylinders and spheres. A thorough comparative evaluation shows state-of-the-art performance while computing such segmentations in near real-time. The second part of the thesis addresses the registration of 3D input data, i.e., consistently aligning input captured from different view poses. Several methods are presented for different types of input data. For the particular application of mapping with micro aerial vehicles where the 3D input data is particularly sparse, a pipeline is proposed that uses the same approximate surface reconstruction to exploit the measurement topology and a surface-to-surface registration algorithm that robustly aligns the data. Optimization of the resulting graph of determined view poses then yields globally consistent 3D maps. For sequences of RGBD data this pipeline is extended to include additional subsampling steps and an initial alignment of the data in local windows in the pose graph. In both cases, comparative evaluations show a robust and fast alignment of the input data
A 0.6V, 8mW 3D Vision Processor for a Navigation Device for the Visually Impaired
This paper presents an energy-efficient computer vision processor for a navigation device for the visually impaired. Utilizing a shared parallel datapath, out-of-order processing and co-optimization with hardware-oriented algorithms, the processor consumes 8mW at 0.6V while processing 30 fps input data stream in real time. The test chip fabricated in 40nm is demonstrated as a core part of a navigation device based on a ToF camera, which successfully detects safe areas and obstacles.Texas Instruments Incorporate
Computational intelligence approaches to robotics, automation, and control [Volume guest editors]
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