This paper presents a fast and robust plane feature extraction and matching technique for RGB-D type sensors. We propose three algorithm components required to utilize the plane features in an online Simultaneous Localization and Mapping (SLAM) problem: fast plane extraction, frame-to-frame constraint estimation, and plane merging. For the fast plane extraction, we estimate local surface normals and curvatures by a simple spherical model and then segment points using a modified flood fill algorithm. In plane parameter estimation, we suggest a new uncertainty estimation method which is robust against the measurement bias, and also introduce a fast boundary modeling method. We associate the plane features based on both the parameters and the spatial coverage, and estimate the stable constraints by the cost function with a regulation term. Also, our plane merging technique provides a way of generating local maps that are useful for estimating loop closure constraints. We have performed real-world experiments at our lab environment. The results demonstrate the efficiency and robustness of the proposed algorithm.

Nylon 6 fibers are used, for the first time, in dye-sensitized solar cells (DSSCs). The overall energy conversion efficiency obtained with 0.18 M nylon 6 reaches 6.2%, which is comparable to that (6.7%) obtained without adding nylon 6 on the day of cell fabrication. However, it is found that the long-term stability of the DSSCs with nylon 6 is superior to that of a reference electrolyte as a result of the complexation of nylon 6 with I(3)(-). Furthermore, nylon 6 is found to be a corrosion inhibitor for silver metal in the electrolyte containing I(3)(-).

In this paper, we develop a new framework for simultaneous localization and mapping (SLAM) based on Rao-Blackwellized particle filters (RBPF), that can be applied to floor-cleaning robots which are equipped with sparse and short-range sensors. To overcome the sensor limitations, the entire region is divided into several local maps, which are assumed to be independent to each other. The local maps are estimated by a local RBPF SLAM, and then the trajectory of the local map origin is estimated by a global RBPF SLAM. To compensate for the severe sensing limitations, we also adopt the assumption that the indoor environments consist of many orthogonal lines. This assumption significantly enhances the filter performance. The proposed SLAM framework is combined with a coverage path planning algorithm, and the resulting robot system is capable of online simultaneous coverage and SLAM. The algorithm was embedded into a real mobile robot platform and tested in a real home environment to assess the robustness of the proposed method.

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