Deep Semantic Classification for 3D LiDAR Data

Robots are expected to operate autonomously in dynamic environments. Understanding the underlying dynamic characteristics of objects is a key enabler for achieving this goal. In this paper, we propose a method for pointwise semantic classification of 3D LiDAR data into three classes: non-movable, movable and dynamic. We concentrate on understanding these specific semantics because they characterize important information required for an autonomous system. Non-movable points in the scene belong to unchanging segments of the environment, whereas the remaining classes corresponds to the changing parts of the scene. The difference between the movable and dynamic class is their motion state. The dynamic points can be perceived as moving, whereas movable objects can move, but are perceived as static. To learn the distinction between movable and non-movable points in the environment, we introduce an approach based on deep neural network and for detecting the dynamic points, we estimate pointwise motion. We propose a Bayes filter framework for combining the learned semantic cues with the motion cues to infer the required semantic classification. In extensive experiments, we compare our approach with other methods on a standard benchmark dataset and report competitive results in comparison to the existing state-of-the-art. Furthermore, we show an improvement in the classification of points by combining the semantic cues retrieved from the neural network with the motion cues.Comment: 8 pages to be published in IROS 201

Tailoring bilberry powder functionality through processing: effects of drying and fractionation on the stability of total polyphenols and anthocyanins.

Bilberries are a rich natural source of phenolic compounds, especially anthocyanins. The press cake obtained during the processing of bilberry juice is a potential source of phytochemicals. The objective of this study was to evaluate different drying techniques and the fractionation of bilberry press cake powder toward obtaining phenolic-rich ingredients for incorporation into value-added food products. The derived powders were dispersed in water and dairy cream, to investigate the effects of drying and fractionation on the dispersibility and solubility of phenolic compounds. The drying techniques, hot air drying and microwave drying, applied on bilberry press cake reduced the content of total phenolics and anthocyanins. The degradation was, however, consistently small and similar for both techniques. The major anthocyanins detected in the samples were stable during drying and fractionation treatments. Fractionation of the press cake powder affected the total apparent phenolic content and composition of the different fractions. The highest phenolic content (55.33 ± 0.06 mg g−1 DW) and highest anthocyanin content (28.15 ± 0.47 mg g−1 DW) were found in the fractions with the smallest particle size (<500 μm), with delphinidin-3-O-galactoside being the most abundant anthocyanin. Dispersibility of all dried powder samples was higher in dairy cream than water, and the highest level of anthocyanins was measured in samples from the powder with the smallest particle size (<500 μm), dispersed in cream. The application of drying, milling and fractionation was found to be a promising approach to transform bilberry press cake into stable and deliverable ingredients that can be used for fortification of food products with high levels of phenolic compounds

A connection between the AαA_\alpha-spectrum and the Lov\'asz theta number

We show that the smallest $\alpha$ so that $\alpha D + (1-\alpha)A \succcurlyeq 0$ is at least $1/\vartheta(\overline{G})$, significantly improving upon a result due to Nikiforov and Rojo (2017). In fact, we display an even stronger connection: if the nonzero entries of $A$ are allowed to vary and those of $D$ vary accordingly, then we show that this smallest $\alpha$ is in fact equal to $1/\vartheta(\overline{G})$. We also show other results obtained as an application of this optimization framework, including a connection to the well-known quadratic formulation for $\omega(G)$ due to Motzkin and Straus (1964)