LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation

Local geometric information, i.e. normal and distribution of points, is crucial for LiDAR-based simultaneous localization and mapping (SLAM) because it provides constraints for data association, which further determines the direction of optimization and ultimately affects the accuracy of localization. However, estimating normal and distribution of points are time-consuming tasks even with the assistance of kdtree or volumetric maps. To achieve fast normal estimation, we look into the structure of LiDAR scan and propose a ring-based fast approximate least squares (Ring FALS) method. With the Ring structural information, estimating the normal requires only the range information of the points when a new scan arrives. To efficiently estimate the distribution of points, we extend the ikd-tree to manage the map in voxels and update the distribution of points in each voxel incrementally while maintaining its consistency with the normal estimation. We further fix the distribution after its convergence to balance the time consumption and the correctness of representation. Based on the extracted and maintained local geometric information, we devise a robust and accurate hierarchical data association scheme where point-to-surfel association is prioritized over point-to-plane. Extensive experiments on diverse public datasets demonstrate the advantages of our system compared to other state-of-the-art methods. Our open source implementation is available at https://github.com/tiev-tongji/LOG-LIO.Comment: 8 pages, 4 figure

Scale Estimation with Dual Quadrics for Monocular Object SLAM

The scale ambiguity problem is inherently unsolvable to monocular SLAM without the metric baseline between moving cameras. In this paper, we present a novel scale estimation approach based on an object-level SLAM system. To obtain the absolute scale of the reconstructed map, we derive a nonlinear optimization method to make the scaled dimensions of objects conforming to the distribution of their sizes in the physical world, without relying on any prior information of gravity direction. We adopt the dual quadric to represent objects for its ability to fit objects compactly and accurately. In the proposed monocular object-level SLAM system, dual quadrics are fastly initialized based on constraints of 2-D detections and fitted oriented bounding box and are further optimized to provide reliable dimensions for scale estimation.Comment: 8 pages, 6 figures, accepted by IROS202

Kernel Nutrient Composition and Antioxidant Ability of Corylus spp. in China

Hazelnut (Corylus) is an important woody oil tree species in economic forests. China, as one of the original countries of native Corylus species, had 8 species and 2 varieties. However, little information is available on the hazelnut nutritional quality of these Chinese Corylus species. In this study, four main wild Corylus species (C. heterophylla Fisch., C. mandshurica Maxim., C. kweichowensis Hu., and C. yunnanensis Franch.) originating in China and one main cultivar of hybrid hazelnut (Corylus heterophylla Fisch. × C. avellana L.) cv. ‘Dawei’ from China were used to analyze the basic nutritional composition (content of oil, fatty acid, protein, saccharide, aminao acid, vitamin C, tocopherol, total phenols, and total flavonoids) and antioxidant ability. The results showed that oil content ranged from 52.97 to 60.88 g/100 g DW and highly unsaturated fatty acid (UFA) content was over 91%. Oleic was the most dominant UFA in these hazelnut kernels, and the relative content was ranging from 71.32 to 85.19%. Compared with other four hazelnut kernels, C. heterophylla Fisch. was the lowest oil content of hazelnut with lower oleic acid content and higher linoleic acid content, obviously. The total protein content ranged from 13.15 to 18.35 g/100 g DW, and all amino acids were detected as hydrate amino acids, but Tryptophan, an essential amino acid, was not detected as free amino acid in these hazelnut kernels. Kernel of C. heterophylla Fisch. was with the highest content of protein and amino acid. Saccharose was the most essential and abundant disaccharide in the hazelnut kernels. C. mandshurica Maxim. was the highest saccharide content among these hazelnut kernels. α-tocopherol was the main type of tocopherol found in the hazelnut kernels. Wild hazelnut kernels generally had higher bioactivity substance content (vitamin C, total tocopherol, total phenol and total flavonoid) and antioxidant capacity. Compared to the four wild hazelnut kernels, the hybrid hazelnut cv. ‘Dawei’ had higher content of oil, oleic acid, α-tocopherol and sugar. Overall, there were great differences in the nutritional composition of different hazelnut species. Wild species are a good source of breeding materials because of their own characteristics in nutrition composition, and the hybrid hazelnut cv. ‘Dawei’ with good quality has the value of commercial promotion

Wind Effects on Dome Structures and Evaluation of CFD Simulations through Wind Tunnel Testing

In the Study, a Series of Wind Tunnel Tests Were Conducted to Investigate Wind Effects Acting on Dome Structures (1/60 Scale) Induced by Straight-Line Winds at a Reynolds Number in the Order of 106. Computational Fluid Dynamics (CFD) Simulations Were Performed as Well, Including a Large Eddy Simulation (LES) and Reynolds-Averaged Navier–Stokes (RANS) Simulation, and their Performances Were Validated by a Comparison with the Wind Tunnel Testing Data. It is Concluded that Wind Loads Generally Increase with Upstream Wind Velocities, and They Are Reduced over Suburban Terrain Due to Ground Friction. the Maximum Positive Pressure Normally Occurs Near the Base of the Dome on the Windward Side Caused by the Stagnation Area and Divergence of Streamlines. the Minimum Suction Pressure Occurs at the Apex of the Dome Because of the Blockage of the Dome and Convergence of Streamlines. Suction Force is the Most Significant among All Wind Loads, and Special Attention Should Be Paid to the Roof Design for Proper Wind Resistance. Numerical Simulations Also Indicate that LES Results Match Better with the Wind Tunnel Testing in Terms of the Distribution Pattern of the Mean Pressure Coefficient on the Dome Surface and Total Suction Force. the Mean and Root-Mean-Square Errors of the Meridian Pressure Coefficient Associated with the LES Are About 60% Less Than Those Associated with RANS Results, and the Error of Suction Force is About 40–70% Less. Moreover, the LES is More Accurate in Predicting the Location of Boundary Layer Separation and Reproducing the Complex Flow Field Behind the Dome, and is Superior in Simulating Vortex Structures Around the Dome to Further Understand the Unsteadiness and Dynamics in the Flow Field

Autopilot Design for Unmanned Surface Vehicle based on CNN and ACO

There is a growing concern to design intelligent controllers for autopiloting unmanned surface vehicles as solution for many naval and civilian requirements. Traditional autopilot’s performance declines due to the uncertainties in hydrodynamics as a result of harsh sailing conditions and sea states. This paper reports the design of a novel nonlinear model predictive controller (NMPC) based on convolutional neural network (CNN) and ant colony optimizer (ACO) which is superior to a linear proportional integral-derivative counterpart. This combination helps the control system to deal with model uncertainties with robustness. The results of simulation and experiment demonstrate the proposed method is more efficient and more capable to guide the vehicle through LOS waypoints particularly in the presence of large disturbances

Identification of candidate genes for soybean seed coat-related traits using QTL mapping and GWAS

Seed coat color is a typical morphological trait that can be used to reveal the evolution of soybean. The study of seed coat color-related traits in soybeans is of great significance for both evolutionary theory and breeding practices. In this study, 180 F10 recombinant inbred lines (RILs) derived from the cross between the yellow-seed coat cultivar Jidou12 (ZDD23040, JD12) and the wild black-seed coat accession Y9 (ZYD02739) were used as materials. Three methods, single-marker analysis (SMA), interval mapping (IM), and inclusive composite interval mapping (ICIM), were used to identify quantitative trait loci (QTLs) controlling seed coat color and seed hilum color. Simultaneously, two genome-wide association study (GWAS) models, the generalized linear model (GLM) and mixed linear model (MLM), were used to jointly identify seed coat color and seed hilum color QTLs in 250 natural populations. By integrating the results from QTL mapping and GWAS analysis, we identified two stable QTLs (qSCC02 and qSCC08) associated with seed coat color and one stable QTL (qSHC08) related to seed hilum color. By combining the results of linkage analysis and association analysis, two stable QTLs (qSCC02, qSCC08) for seed coat color and one stable QTL (qSHC08) for seed hilum color were identified. Upon further investigation using Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, we validated the previous findings that two candidate genes (CHS3C and CHS4A) reside within the qSCC08 region and identified a new QTL, qSCC02. There were a total of 28 candidate genes in the interval, among which Glyma.02G024600, Glyma.02G024700, and Glyma.02G024800 were mapped to the glutathione metabolic pathway, which is related to the transport or accumulation of anthocyanin. We considered the three genes as potential candidate genes for soybean seed coat-related traits. The QTLs and candidate genes detected in this study provide a foundation for further understanding the genetic mechanisms underlying soybean seed coat color and seed hilum color and are of significant value in marker-assisted breeding

TiEV: The Tongji Intelligent Electric Vehicle in the Intelligent Vehicle Future Challenge of China

TiEV is an autonomous driving platform implemented by Tongji University of China. The vehicle is drive-by-wire and is fully powered by electricity. We devised the software system of TiEV from scratch, which is capable of driving the vehicle autonomously in urban paths as well as on fast express roads. We describe our whole system, especially novel modules of probabilistic perception fusion, incremental mapping, the 1st and the 2nd planning and the overall safety concern. TiEV finished 2016 and 2017 Intelligent Vehicle Future Challenge of China held at Changshu. We show our experiences on the development of autonomous vehicles and future trends

Simulation research of coal reservoir pressure variation law

Accurately quantifying the real-time variation of reservoir pressure is the key factor to predict the variation characteristics of reservoir porosity and permeability and control the gas recovery effect. In order to further explore the characteristics of coal reservoir pressure dynamic variation in Baode area and its impact on the productivity of coalbed methane (CBM) wells, based on production dynamic data, a real-time simulation and detection technology of coal reservoir pressure was established while quantitatively described the pressure dynamics of saturated and unsaturated CBM reservoirs in Baode area, by considering the influence of positive and negative effects. By comparing the productivity under different pressure drop conditions, the prediction basis and theoretical guidance were provided for production. During the exploitation of CBM wells, the average reservoir pressure of saturated CBM reservoirs decreases linearly. Taking the average daily gas production of a single well as the selected standard, the pressure drop rate is divided into three types: fast falling type (more than 0.096 MPa/m), steady falling type (0.063～0.096 MPa/m) and slow falling type (less than 0.063 MPa/m). The productivity of CBM wells with steady-descent type (4648 m3/d) pressure drop system is significantly better than that of fast-descent type (2531 m3/d) and slow-descent type (2968 m3/d). Fast-descent type CBM wells have the lowest productivity. In the single-phase water flow stage of the undersaturated coalbed methane reservoir in the early stage of development, the average reservoir pressure drops rapidly, and the pressure drop rate is divided into three types: fast falling type (more than 0.38 MPa/m), steady falling type (0.228—0.38 MPa/m) and slow falling type (less than 0.228 MPa/m). The productivity of CBM wells with fast-descent type (526 m3/d) pressure drop system is significantly lower than that of steady-descent type (1021 m3/d) and slow-descent type (1054 m3/d). As the reservoir pressure drops to the critical desorption pressure, the reservoir pressure decreases linearly and slowly in the gas-water two-phase flow stage. It is suggested that the initial pressure drop rate of saturated CBM reservoirs should be maintained at 0.063～0.096MPa/m, and the initial pressure drop rate of unsaturated CBM reservoirs should not exceed 0.38 MPa/m in Baode area