Robust haptic exploration of remote environments represented by streamed point cloud data

Kim, Seokyeol; Park, Jinah

doi:10.1109/whc.2017.7989928

Cited by 3 publications

(1 citation statement)

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“…Literature of supported featuresto what can be aligned to the robotic component using depth-point alignment. This can lead to issues when dealing with sparse or noisy depth data.Kim and Park[19] focused their work on developing an algorithm to fit haptic surfaces to point clouds captured with RGB-D sensors. The method proposed by them incorporates the use of a haptic device to allow the user to have a sense of interaction with the physical components of the virtual environment.…”

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confidence: 99%

Dynamic Rendering of Remote Indoor Environments Using Real-Time Point Cloud Data

Lesniak

Tucker

2018

Journal of Computing and Information Science in Engineering

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Modern color and depth (RGB-D) sensing systems are capable of reconstructing convincing virtual representations of real world environments. These virtual reconstructions can be used as the foundation for virtual reality (VR) and augmented reality environments due to their high-quality visualizations. However, a main limitation of modern virtual reconstruction methods is the time it takes to incorporate new data and update the virtual reconstruction. This delay prevents the reconstruction from accurately rendering dynamic objects or portions of the environment (like an engineer performing an inspection of a machinery or laboratory space). The authors propose a multisensor method to dynamically capture objects in an indoor environment. The method automatically aligns the sensors using modern image homography techniques, leverages graphics processing units (GPUs) to process the large number of independent RGB-D data points, and renders them in real time. Incorporating and aligning multiple sensors allows a larger area to be captured from multiple angles, providing a more complete virtual representation of the physical space. Performing processing on GPU's leverages the large number of processing cores available to minimize the delay between data capture and rendering. A case study using commodity RGB-D sensors, computing hardware, and standard transmission control protocol internet connections is presented to demonstrate the viability of the proposed method.

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confidence: 99%