Anthocyanin-rich edible flowers, current understanding of a potential new trend in dietary patterns.

Funding Information: The authors would like to thank the following institutions: FCT- Fundação para a Ciência e a Tecnologia through the unit funding UIDB/50006/2020 and project AnthoE.Flos - 2022.01014. PTDC; European Regional Development Fund ( ERDF ), through the NORTE 2020 (Programa Operacional Regional do Norte 2014/2020) for the AgriFood XXI I&D&I project (NORTE-01-0145-FEDER-000041). H.O. and A.L.F. would like to also acknowledge their CEEC contracts 2021.00002. CEECIND and CEECIND/00029/2018, respectively. Funding Information: The authors would like to thank the following institutions: FCT- Fundação para a Ciência e a Tecnologia through the unit funding UIDB/50006/2020 and project AnthoE.Flos - 2022.01014. PTDC; European Regional Development Fund (ERDF), through the NORTE 2020 (Programa Operacional Regional do Norte 2014/2020) for the AgriFood XXI I&D&I project (NORTE-01-0145-FEDER-000041). H.O. and A.L.F. would like to also acknowledge their CEEC contracts 2021.00002. CEECIND and CEECIND/00029/2018, respectively. Publisher Copyright: © 2023 Elsevier LtdBackground: Among the many sources of anthocyanins, edible flowers are regaining interest for both consumers and researchers due to their nutritional profile and the need for even more healthy dietary alternatives. In such context, anthocyanin-rich edible flowers may be one of the most interesting groups of such cultivars but also of anthocyanins source. Scope and approach: In this review, we discuss the latest findings regarding such type of edible flowers, from their consumption patterns to their nutritional and anthocyanins composition, their reported health benefits, the challenges about the consumption of edible flowers and the future research necessities on this promising thematic. Key findings and conclusions: Anthocyanins have become a key group of natural compounds during the last years due to their broad applications in different areas. From a nutritional and health perspective, these compounds have been showing potential roles against different pathologies. The excellent aroma, taste and appearance of anthocyanin-rich edible flowers turns meals more appealing to consumers. Moreover, their nutritional profile, bioactive properties, and health benefits, encourages the development of functional foods with nutraceutical purposes, thus promoting the consumption of these type of edible flowers worldwide. Further knowledge in food processing methods is a key factor on the comeback and the addition of anthocyanin-rich edible flowers to our dietary habits.publishersversionpublishe

A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis

Indoor mobile mapping techniques are important for indoor navigation and indoor modeling. As an efficient method, Simultaneous Localization and Mapping (SLAM) based on Light Detection and Ranging (LiDAR) has been applied for fast indoor mobile mapping. It can quickly construct high-precision indoor maps in a certain small region. However, with the expansion of the mapping area, SLAM-based mapping methods face many difficulties, such as loop closure detection, large amounts of calculation, large memory occupation, and limited mapping precision. In this paper, we propose a distributed indoor mapping scheme based on control-network-aided SLAM to solve the problem of mapping for large-scale environments. Its effectiveness is analyzed from the relative accuracy and absolute accuracy of the mapping results. The experimental results show that the relative accuracy can reach 0.08 m, an improvement of 49.8% compared to the mapping result without loop closure. The absolute accuracy can reach 0.13 m, which proves the method’s feasibility for distributed mapping. The accuracies under different numbers of control points are also compared to find the suitable structure of the control network

Characteristics of GLONASS Inter-frequency Code Bias and Its Application on Wide-lane Ambiguity Resolution

GLONASS inter-frequency code biases (IFCBs) vary with receiver manufacturers,firmware versions,and antenna types.IFCBs are hardly corrected or modeled precisely,so that Hatch-Melbourne-Wübbena (HMW) combination observation contains a systemic bias and cannot applied into GLONASS wide-lane ambiguity resolution.Utilizing the residuals of GLONASS HMW combination observations,we propose an algorithm to estimate IFCB of different sites (DS-IFCB).The experiment results show that DS-IFCB is long term stability and the sizes of DS-IFCBs in some homogeneous baselines (composed by same type of devices,i.e.receiver type,version and antenna) are larger than 0.5 meters.In order to achieve wide-lane ambiguities in real-time,DS-IFCBs,estimated with previous observations,are used as priors to cancel IFCBs in current observations.After DS-IFCB offset,both the success rate and correct rate of GLONASS wide-lane ambiguity resolutions are improved,regardless of whether baselines are equipped with homogeneous devices.The correct rates of all baselines are higher than 98%

Real-Time Scan-to-Map Matching Localization System Based on Lightweight Pre-Built Occupancy High-Definition Map

High-precision and robust localization in GNSS-denied areas is crucial for autonomous vehicles and robots. Most state-of-the-art localization methods are based on simultaneous localization and mapping (SLAM) with a camera or light detection and ranging (LiDAR). However, SLAM will suffer from drift during long-term running without loop closure or prior constraints. Lightweight, high-precision environmental maps have gradually become an indispensable part of future autonomous driving. In order to solve the problem of real-time global localization for autonomous vehicles and robots, we propose a precise and robust LiDAR localization system based on a pre-built, occupied high-definition (HD) map called the Extended QuadTree (EQT) map. It makes use of a planar quadtree for block division and a Z-sequence index structure within the block cells. Then, a four-level occupancy probability cell value model is adopted. It will save about eight times the storage space compared with Google Cartographer, and the EQT map can be extended to store other information. For efficient scan-to-map matching with our specialized EQT map, the Bursa linearized model is used in the Gauss–Newton iteration of our algorithm, which makes the calculation of partial derivatives fast. All the above improvements lead to optimal storage and efficient querying for real-time scan-to-map matching localization. Field tests in an industrial park and road environment prove that positioning accuracy of about 6–13 cm and attitude accuracy of about 0.15° were achieved using a VLP-16 LiDAR. They also show that the method proposed in this paper is significantly better than the NDT method. For the long and narrow environment of an underground mine tunnel, high-resolution maps are also helpful for accurate and robust localization

Improvement Analysis of a Height‐Deviation Compensation‐Based Linear Interpolation Method for Multi‐Station Regional Troposphere

Abstract In network real‐time kinematic positioning of multi‐reference station, the spatial and temporal distribution of tropospheric delay is affected by both horizontal and elevation. The traditional modeling strategy of regional troposphere takes more consideration of the horizontal factor, and the incomplete consideration of the elevation factor will lead to the problem of reduced modeling accuracy, especially in the face of the scene with large regional height deviation. Based on the traditional linear interpolation method (LIM), a simple and effective height‐deviation compensation‐based linear interpolation method (HCLIM) for regional tropospheric is proposed. The modeling accuracy of troposphere and the positioning accuracy of user RTK in large height deviation region are significantly improved. The method was verified based on six experimental subnets with large height deviations from a provincial continuously operating GNSS reference stations network in central China. The results showed that: For GPS satellite modeling, compared with the traditional LIM method, the average modeling accuracy improvement rate of HCLIM method is (84.5%, 75.5%, 59.3%, 26.7%) in the elevation angle range of (10–30°/30–40°/40–50°/50–90°). For BDS satellite, the average modeling accuracy improvement rate of HCLIM method in the above four elevation angles is (83.3%, 70%, 50%, 23.5%). For the positioning performance of user RTK, The horizontal positioning accuracy and RTK fixing rate were similar under the two methods, while HCLIM method showed only slight improvement. However, in the U direction, LIM method showed obvious systematic bias, while HCLIM method showed consistent positioning accuracy, which was improved to 82.8% compared with LIM method

2D LiDAR SLAM Back-End Optimization with Control Network Constraint for Mobile Mapping

Simultaneous localization and mapping (SLAM) has been investigated in the field of robotics for two decades, as it is considered to be an effective method for solving the positioning and mapping problem in a single framework. In the SLAM community, the Extended Kalman Filter (EKF) based SLAM and particle filter SLAM are the most mature technologies. After years of development, graph-based SLAM is becoming the most promising technology and a lot of progress has been made recently with respect to accuracy and efficiency. No matter which SLAM method is used, loop closure is a vital part for overcoming the accumulated errors. However, in 2D Light Detection and Ranging (LiDAR) SLAM, on one hand, it is relatively difficult to extract distinctive features in LiDAR scans for loop closure detection, as 2D LiDAR scans encode much less information than images; on the other hand, there is also some special mapping scenery, where no loop closure exists. Thereby, in this paper, instead of loop closure detection, we first propose the method to introduce extra control network constraint (CNC) to the back-end optimization of graph-based SLAM, by aligning the LiDAR scan center with the control vertex of the presurveyed control network to optimize all the poses of scans and submaps. Field tests were carried out in a typical urban Global Navigation Satellite System (GNSS) weak outdoor area. The results prove that the position Root Mean Square (RMS) error of the selected key points is 0.3614 m, evaluated with a reference map produced by Terrestrial Laser Scanner (TLS). Mapping accuracy is significantly improved, compared to the mapping RMS of 1.6462 m without control network constraint. Adding distance constraints of the control network to the back-end optimization is an effective and practical method to solve the drift accumulation of LiDAR front-end scan matching