scholarly journals Large-Scale LiDAR SLAM with Factor Graph Optimization on High-Level Geometric Features

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3445
Author(s):  
Krzysztof Ćwian ◽  
Michał R. Nowicki ◽  
Jan Wietrzykowski ◽  
Piotr Skrzypczyński
Keyword(s):  
Large Scale ◽  
Point Clouds ◽  
Factor Graph ◽  
Optimization Approach ◽  
Geometric Features ◽  
Visual Slam ◽  
Loop Closure ◽  
Line Segments ◽  
Graph Optimization ◽  
High Level

Although visual SLAM (simultaneous localization and mapping) methods obtain very accurate results using optimization of residual errors defined with respect to the matching features, the SLAM systems based on 3-D laser (LiDAR) data commonly employ variants of the iterative closest points algorithm and raw point clouds as the map representation. However, it is possible to extract from point clouds features that are more spatially extended and more meaningful than points: line segments and/or planar patches. In particular, such features provide a natural way to represent human-made environments, such as urban and mixed indoor/outdoor scenes. In this paper, we perform an analysis of the advantages of a LiDAR-based SLAM that employs high-level geometric features in large-scale urban environments. We present a new approach to the LiDAR SLAM that uses planar patches and line segments for map representation and employs factor graph optimization typical to state-of-the-art visual SLAM for the final map and trajectory optimization. The new map structure and matching of features make it possible to implement in our system an efficient loop closure method, which exploits learned descriptors for place recognition and factor graph for optimization. With these improvements, the overall software structure is based on the proven LOAM concept to ensure real-time operation. A series of experiments were performed to compare the proposed solution to the open-source LOAM, considering different approaches to loop closure computation. The results are compared using standard metrics of trajectory accuracy, focusing on the final quality of the estimated trajectory and the consistency of the environment map. With some well-discussed reservations, our results demonstrate the gains due to using the high-level features in the full-optimization approach in the large-scale LiDAR SLAM.

scholarly journals A LiDAR/Visual SLAM Backend with Loop Closure Detection and Graph Optimization

2021 ◽  
Vol 13 (14) ◽  
pp. 2720
Author(s):  
Shoubin Chen ◽  
Baoding Zhou ◽  
Changhui Jiang ◽  
Weixing Xue ◽  
Qingquan Li
Keyword(s):  
Point Clouds ◽  
Visual Features ◽  
Visual Slam ◽  
Key Factors ◽  
Loop Closure ◽  
Loop Closure Detection ◽  
Active Sensor ◽  
Localization And Mapping ◽  
Graph Optimization ◽  
Pose Graph Optimization

LiDAR (light detection and ranging), as an active sensor, is investigated in the simultaneous localization and mapping (SLAM) system. Typically, a LiDAR SLAM system consists of front-end odometry and back-end optimization modules. Loop closure detection and pose graph optimization are the key factors determining the performance of the LiDAR SLAM system. However, the LiDAR works at a single wavelength (905 nm), and few textures or visual features are extracted, which restricts the performance of point clouds matching based loop closure detection and graph optimization. With the aim of improving LiDAR SLAM performance, in this paper, we proposed a LiDAR and visual SLAM backend, which utilizes LiDAR geometry features and visual features to accomplish loop closure detection. Firstly, the bag of word (BoW) model, describing the visual similarities, was constructed to assist in the loop closure detection and, secondly, point clouds re-matching was conducted to verify the loop closure detection and accomplish graph optimization. Experiments with different datasets were carried out for assessing the proposed method, and the results demonstrated that the inclusion of the visual features effectively helped with the loop closure detection and improved LiDAR SLAM performance. In addition, the source code, which is open source, is available for download once you contact the corresponding author.

scholarly journals Incremental 3-D pose graph optimization for SLAM algorithm without marginalization

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142092530
Author(s):  
Feng Youyang ◽  
Wang Qing ◽  
Yang Gaochao
Keyword(s):  
Optimization Algorithm ◽  
Real World ◽  
Bayes Factor ◽  
Simulated Data ◽  
Schur Complement ◽  
Factor Graph ◽  
Data Sets ◽  
Loop Closure ◽  
Graph Optimization ◽  
Pose Graph Optimization

Pose graph optimization algorithm is a classic nonconvex problem which is widely used in simultaneous localization and mapping algorithm. First, we investigate previous contributions and evaluate their performances using KITTI, Technische Universität München (TUM), and New College data sets. In practical scenario, pose graph optimization starts optimizing when loop closure happens. An estimated robot pose meets more than one loop closures; Schur complement is the common method to obtain sequential pose graph results. We put forward a new algorithm without managing complex Bayes factor graph and obtain more accurate pose graph result than state-of-art algorithms. In the proposed method, we transform the problem of estimating absolute poses to the problem of estimating relative poses. We name this incremental pose graph optimization algorithm as G-pose graph optimization algorithm. Another advantage of G-pose graph optimization algorithm is robust to outliers. We add loop closure metric to deal with outlier data. Previous experiments of pose graph optimization algorithm use simulated data, which do not conform to real world, to evaluate performances. We use KITTI, TUM, and New College data sets, which are obtained by real sensor in this study. Experimental results demonstrate that our proposed incremental pose graph algorithm model is stable and accurate in real-world scenario.

scholarly journals WEAKLY SUPERVISED SEGMENTATION-AIDED CLASSIFICATION OF URBAN SCENES FROM 3D LIDAR POINT CLOUDS

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 151-157 ◽  
Author(s):  
S. Guinard ◽  
L. Landrieu
Keyword(s):  
Large Scale ◽  
Conditional Random Field ◽  
Level Structure ◽  
Point Clouds ◽  
Semantic Classification ◽  
Urban Scenes ◽  
Weakly Supervised ◽  
High Level ◽  
3D Lidar

We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes, partionning the scene into geometrically-homogeneous segments which size is determined by the local complexity. This segmentation can be integrated into a conditional random field classifier (CRF) in order to capture the high-level structure of the scene. For each cluster, this allows us to aggregate the noisy predictions of a weakly-supervised classifier to produce a higher confidence data term. We demonstrate the improvement provided by our method over two publicly-available large-scale data sets.

EVALUATION STUDY OF SYSTOLIC ARRAY PROCESSORS OPTIMIZATION AND MAPPING ON k-LUT FPGA DEVICES

2013 ◽  
Vol 22 (04) ◽  
pp. 1350025
Author(s):  
STAVROS P. DOKOUZYANNIS ◽  
ARGIRIS P. MOKIOS
Keyword(s):  
Systolic Array ◽  
High Speed ◽  
Large Scale ◽  
Logic Synthesis ◽  
Evaluation Study ◽  
Optimization Approach ◽  
Array Processors ◽  
Field Programmable ◽  
High Level ◽  
Selection Of

This paper analyzes the design automation of embedded Systolic Array Processors (SAPs), into large scale Field Programmable Gate Array (FPGA) devices. SAPs are hardware implementations of a class of iterative, high-level language algorithms, for applications where the high-speed of processing has the principal meaning of a design. Embedding SAPs onto FPGAs is a complex process. The optimization phase in this process reduces the SAP significantly, thus less FPGA area is occupied by the embedded design, without any loss in the final performance. The present paper examines the effect of Projection Vectors (PVs) and Task Scheduling Vectors (TSVs) on the optimization process. Two optimization approaches are examined, namely technology mapping using FlowMap and Flowpack algorithms and optimization via logic synthesis using Xilinx Synthesis Tool. The multiplication of matrices, with entries being up to 32-bit integer vectors, has been taken as a sample space for the experiments conducted. The results, confirm that the selection of PV and TSV greatly affects the number of input/output signal connections of the FPGA, while the selection of an optimization approach affects the final number of logic resources occupied on the targeted device.

scholarly journals STANDARDS-BASED SERVICES FOR BIG SPATIO-TEMPORAL DATA

2016 ◽  
Vol XLI-B4 ◽  
pp. 691-699
Author(s):  
P. Baumann ◽  
V. Merticariu ◽  
A. Dumitru ◽  
D. Misev
Keyword(s):  
Data Model ◽  
Large Scale ◽  
Point Clouds ◽  
Current Status ◽  
Temporal Data ◽  
Large Scale Data ◽  
Spatio Temporal ◽  
High Level ◽  
Immense Potential ◽  
Scale Data

With the unprecedented availability of continuously updated measured and generated data there is an immense potential for getting new and timely insights &ndash; yet, the value is not fully leveraged as of today. The quest is up for high-level service interfaces for dissecting datasets and rejoining them with other datasets &ndash; ultimately, to allow users to ask "any question, anytime, on any size" enabling them to "build their own product on the go". <br><br> With OGC Coverages, a concrete, interoperable data model has been established which unifies n-D spatio-temporal regular and irregular grids, point clouds, and meshes. The Web Coverage Service (WCS) suite provides versatile streamlined coverage functionality ranging from simple access to flexible spatio-temporal analytics. Flexibility and scalability of the WCS suite has been demonstrated in practice through massive services run by large-scale data centers. We present the current status in OGC Coverage data and service models, contrast them to related work, and describe a scalable implementation based on the rasdaman array engine.

scholarly journals STANDARDS-BASED SERVICES FOR BIG SPATIO-TEMPORAL DATA

2016 ◽  
Vol XLI-B4 ◽  
pp. 691-699 ◽  
Author(s):  
P. Baumann ◽  
V. Merticariu ◽  
A. Dumitru ◽  
D. Misev
Keyword(s):  
Data Model ◽  
Large Scale ◽  
Point Clouds ◽  
Current Status ◽  
Temporal Data ◽  
Large Scale Data ◽  
Spatio Temporal ◽  
High Level ◽  
Immense Potential ◽  
Scale Data

With the unprecedented availability of continuously updated measured and generated data there is an immense potential for getting new and timely insights &ndash; yet, the value is not fully leveraged as of today. The quest is up for high-level service interfaces for dissecting datasets and rejoining them with other datasets &ndash; ultimately, to allow users to ask "any question, anytime, on any size" enabling them to "build their own product on the go". &lt;br&gt;&lt;br&gt; With OGC Coverages, a concrete, interoperable data model has been established which unifies n-D spatio-temporal regular and irregular grids, point clouds, and meshes. The Web Coverage Service (WCS) suite provides versatile streamlined coverage functionality ranging from simple access to flexible spatio-temporal analytics. Flexibility and scalability of the WCS suite has been demonstrated in practice through massive services run by large-scale data centers. We present the current status in OGC Coverage data and service models, contrast them to related work, and describe a scalable implementation based on the rasdaman array engine.

Social inequality in the evolution of human societies

2019 ◽  
pp. 98-120
Author(s):  
Georgi Derluguian
Keyword(s):  
Social Inequality ◽  
Large Scale ◽  
Human Beings ◽  
Mass Violence ◽  
Public And Private ◽  
Tangible Assets ◽  
Transition To Agriculture ◽  
New Institutions ◽  
High Level ◽  
Political Economic

The author develops ideas about the origin of social inequality during the evolution of human societies and reflects on the possibilities of its overcoming. What makes human beings different from other primates is a high level of egalitarianism and altruism, which contributed to more successful adaptability of human collectives at early stages of the development of society. The transition to agriculture, coupled with substantially increasing population density, was marked by the emergence and institutionalisation of social inequality based on the inequality of tangible assets and symbolic wealth. Then, new institutions of warfare came into existence, and they were aimed at conquering and enslaving the neighbours engaged in productive labour. While exercising control over nature, people also established and strengthened their power over other people. Chiefdom as a new type of polity came into being. Elementary forms of power (political, economic and ideological) served as a basis for the formation of early states. The societies in those states were characterised by social inequality and cruelties, including slavery, mass violence and numerous victims. Nowadays, the old elementary forms of power that are inherent in personalistic chiefdom are still functioning along with modern institutions of public and private bureaucracy. This constitutes the key contradiction of our time, which is the juxtaposition of individual despotic power and public infrastructural one. However, society is evolving towards an ever more efficient combination of social initiatives with the sustainability and viability of large-scale organisations.

scholarly journals Arabidopsis Genes Essential for Seedling Viability: Isolation of Insertional Mutants and Molecular Cloning

Genetics ◽  
2001 ◽  
Vol 159 (4) ◽  
pp. 1765-1778
Author(s):  
Gregory J Budziszewski ◽  
Sharon Potter Lewis ◽  
Lyn Wegrich Glover ◽  
Jennifer Reineke ◽  
Gary Jones ◽  
...  
Keyword(s):  
Large Scale ◽  
Protein Translocation ◽  
Gene Families ◽  
Mutant Phenotype ◽  
Lethal Mutant ◽  
A Genome ◽  
Genes Encoding ◽  
High Level ◽  
Mutant Lines ◽  
Genome Scale

Abstract We have undertaken a large-scale genetic screen to identify genes with a seedling-lethal mutant phenotype. From screening ~38,000 insertional mutant lines, we identified &gt;500 seedling-lethal mutants, completed cosegregation analysis of the insertion and the lethal phenotype for &gt;200 mutants, molecularly characterized 54 mutants, and provided a detailed description for 22 of them. Most of the seedling-lethal mutants seem to affect chloroplast function because they display altered pigmentation and affect genes encoding proteins predicted to have chloroplast localization. Although a high level of functional redundancy in Arabidopsis might be expected because 65% of genes are members of gene families, we found that 41% of the essential genes found in this study are members of Arabidopsis gene families. In addition, we isolated several interesting classes of mutants and genes. We found three mutants in the recently discovered nonmevalonate isoprenoid biosynthetic pathway and mutants disrupting genes similar to Tic40 and tatC, which are likely to be involved in chloroplast protein translocation. Finally, we directly compared T-DNA and Ac/Ds transposon mutagenesis methods in Arabidopsis on a genome scale. In each population, we found only about one-third of the insertion mutations cosegregated with a mutant phenotype.

scholarly journals ClusterSLAM: A SLAM backend for simultaneous rigid body clustering and motion estimation

2021 ◽  
Author(s):  
Jiahui Huang ◽  
Sheng Yang ◽  
Zishuo Zhao ◽  
Yu-Kun Lai ◽  
Shi-Min Hu
Keyword(s):  
Motion Estimation ◽  
Rigid Body ◽  
Iterative Scheme ◽  
Dynamic Environments ◽  
Rigid Bodies ◽  
Factor Graph ◽  
Agglomerative Clustering ◽  
Multiple Objects ◽  
Graph Optimization ◽  
Dynamic Objects

AbstractWe present a practical backend for stereo visual SLAM which can simultaneously discover individual rigid bodies and compute their motions in dynamic environments. While recent factor graph based state optimization algorithms have shown their ability to robustly solve SLAM problems by treating dynamic objects as outliers, their dynamic motions are rarely considered. In this paper, we exploit the consensus of 3D motions for landmarks extracted from the same rigid body for clustering, and to identify static and dynamic objects in a unified manner. Specifically, our algorithm builds a noise-aware motion affinity matrix from landmarks, and uses agglomerative clustering to distinguish rigid bodies. Using decoupled factor graph optimization to revise their shapes and trajectories, we obtain an iterative scheme to update both cluster assignments and motion estimation reciprocally. Evaluations on both synthetic scenes and KITTI demonstrate the capability of our approach, and further experiments considering online efficiency also show the effectiveness of our method for simultaneously tracking ego-motion and multiple objects.

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