Atlanta World Research Articles

S-VIO: Exploiting Structural Constraints for RGB-D Visual Inertial Odometry

Vision-based localization is an essential problem for autonomous systems while the performance of vision-based odometry degrades in the challenging scenario. This letter presents S-VIO, an RGB-D visual inertial odometry (VIO) which fully uses multi-sensor measurements (i.e. depth, RGB and IMU), heterogeneous landmarks (i.e. points, lines and planes) and structural regularity of the environment to obtain a robust and accurate localization result. In order to detect the underlying structural regularity of the environment, a two-step Atlanta world inference method is proposed. Leveraging the gravity direction estimated by the VIO system, the proposed algorithm first generates horizontal Atlanta axis hypotheses from a set of recently optimized plane landmarks. Then the following plane landmarks and line clusters are used to filter out the occasionally observed axes based on the persistence of the observation. The remaining axes will survive and be saved in the Atlanta map for future re-observations. Particularly, an efficient mined-and-stabbed (MnS) method is applied to classify the structural line and extract the vanishing point from each line cluster. In addition, a closed-form initialization method for the structural line feature is proposed, which leverages the known direction to obtain a better initial estimation. Integrated with the above novelties, S-VIO is tested on two public real-world RGB-D inertial datasets. Experiments demonstrate that S-VIO has better accuracy and robustness compared to state-of-the-art VIO and RGB-D VIO algorithms. The code and the supplementary material will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/oranfire/SVIO</uri> .

Robust Visual Odometry Leveraging Mixture of Manhattan Frames in Indoor Environments.

We propose a robust RGB-Depth (RGB-D) Visual Odometry (VO) system to improve the localization performance of indoor scenes by using geometric features, including point and line features. Previous VO/Simultaneous Localization and Mapping (SLAM) algorithms estimate the low-drift camera poses with the Manhattan World (MW)/Atlanta World (AW) assumption, which limits the applications of such systems. In this paper, we divide the indoor environments into two different scenes: MW and non-MW scenes. The Manhattan scenes are modeled as a Mixture of Manhattan Frames, in which each Manhattan Frame in itself defines a Manhattan World of a specific orientation. Moreover, we provide a method to detect Manhattan Frames (MFs) using the dominant directions extracted from the parallel lines. Our approach is designed with lower computational complexity than existing techniques using planes to detect Manhattan Frame (MF). For MW scenes, we separately estimate rotational and translational motion. A novel method is proposed to estimate the drift-free rotation using MF observations, unit direction vectors of lines, and surface normal vectors. Then, the translation part is recovered from point-line tracking. In non-MW scenes, the tracked and matched dominant directions are combined with the point and line features to estimate the full 6 degree of freedom (DoF) camera poses. Additionally, we exploit the rotation constraints generated from the multi-view dominant directions observations. The constraints are combined with the reprojection errors of points and lines to refine the camera pose through local map bundle adjustment. Evaluations on both synthesized and real-world datasets demonstrate that our approach outperforms state-of-the-art methods. On synthesized datasets, average localization accuracy is 1.5 cm, which is equivalent to state-of-the-art methods. On real-world datasets, the average localization accuracy is 1.7 cm, which outperforms the state-of-the-art methods by 43%. Our time consumption is reduced by 36%.

Sigma-FP: Robot Mapping of 3D Floor Plans With an RGB-D Camera Under Uncertainty

This letter presents Sigma-FP, a novel 3D reconstruction method to obtain the floor plan of a multi-room environment from a sequence of RGB-D images captured by a wheeled mobile robot. For each input image, the planar patches of visible walls are extracted and subsequently characterized by a multivariate Gaussian distribution in the convenient Plane Parameter Space. Then, accounting for the probabilistic nature of the robot localization, we transform and combine the planar patches from the camera frame into a 3D global model, where the planar patches include both the plane estimation uncertainty and the propagation of the robot pose uncertainty. Additionally, processing depth data, we detect openings (doors and windows) in the wall, which are also incorporated in the 3D global model to provide a more realistic representation. Experimental results, in both real-world and synthetic environments, demonstrate that our method outperforms state-of-the-art methods, both in time and accuracy, while just relying on Atlanta world assumption.

Vectorized indoor surface reconstruction from 3D point cloud with multistep 2D optimization

Vectorized reconstruction from indoor point cloud has attracted increasing attention in recent years due to its high regularity and low memory consumption. Compared with aerial mapping of outdoor urban environments, indoor point cloud generated by LiDAR scanning or image-based 3D reconstruction usually contain more clutter and missing areas, which greatly increase the difficulty of vectorized reconstruction. In this paper, we propose an effective multistep pipeline to reconstruct vectorized models from indoor point cloud without the Manhattan or Atlanta world assumptions. The core idea behind our method is the combination of a sequence of 2D segment or cell assembly problems that are defined as global optimizations while reducing the reconstruction complexity and enhancing the robustness to different scenes. The proposed method includes a semantic segmentation stage and a reconstruction stage. First, we segment the permanent structures of indoor scenes, including ceilings, floors, walls and cylinders, from the input data, and then, we reconstruct these structures in sequence. The floorplan is first generated by detecting wall planes and selecting optimal subsets of projected wall segments with Integer Linear Programming (ILP), followed by constructing a 2D arrangement and recovering the ceiling and floor structures by Markov Random Filed (MRF) labeling on the arrangement. Finally, the wall structures are modeled by lifting each edge of the arrangement to a proper height by means of another global optimization. Merging the respective results yields the final model. The experimental results show that the proposed method could obtain accurate and compact vectorized models on both precise LiDAR data and defect-laden MVS data compared with other state-of-the-art approaches.

Urban Scene LOD Vectorized Modeling From Photogrammetry Meshes.

Urban scene modeling is a challenging task for the photogrammetry and computer vision community due to its large scale, structural complexity, and topological delicacy. This paper presents an efficient multistep modeling framework for large-scale urban scenes from aerial images. It takes aerial images and a textured 3D mesh model generated by an image-based modeling system as the input and outputs compact polygon models with semantics at different levels of detail (LODs). Based on the key observation that urban buildings usually have piecewise planar rooftops and vertical walls, we propose a segment-based modeling method, which consists of three major stages: scene segmentation, roof contour extraction, and building modeling. By combining the deep neural network predictions with geometric constraints of the 3D mesh, the scene is first segmented into three classes. Then, for each building mesh, the 2D line segments are detected and used to slice the ground into polygon cells, followed by assigning each cell a roof plane via a MRF optimization. Finally, the LOD model is obtained by extruding cells to their corresponding planes. Compared with direct modeling in 3D space, we transform the mesh into a uniform 2D image grid representation and most of the modeling work is performed in 2D space, which has the advantages of low computational complexity and high robustness. In addition, our method doesn't require any global prior, such as the Manhattan or Atlanta world assumption, making it flexible to model scenes with different characteristics and complexity. Experiments on both single buildings and large-scale urban scenes demonstrate that by combining 2D photometric with 3D geometric information, the proposed algorithm is robust and efficient in urban scene LOD vectorized modeling compared with the state-of-the-art approaches.

Linear RGB-D SLAM for Structured Environments.

We propose a new linear RGB-D simultaneous localization and mapping (SLAM) formulation by utilizing planar features of the structured environments. The key idea is to understand a given structured scene and exploit its structural regularities such as the Manhattan world. This understanding allows us to decouple the camera rotation by tracking structural regularities, which makes SLAM problems free from being highly nonlinear. Additionally, it provides a simple yet effective cue for representing planar features, which leads to a linear SLAM formulation. Given an accurate camera rotation, we jointly estimate the camera translation and planar landmarks in the global planar map using a linear Kalman filter. Our linear SLAM method, called L-SLAM, can understand not only the Manhattan world but the more general scenario of the Atlanta world, which consists of a vertical direction and a set of horizontal directions orthogonal to the vertical direction. To this end, we introduce a novel tracking-by-detection scheme that infers the underlying scene structure by Atlanta representation. With efficient Atlanta representation, we formulate a unified linear SLAM framework for structured environments. We evaluate L-SLAM on a synthetic dataset and RGB-D benchmarks, demonstrating comparable performance to other state-of-the-art SLAM methods without using expensive nonlinear optimization. We assess the accuracy of L-SLAM on a practical application of augmented reality.

Nathan Cardon. A Dream of the Future: Race, Empire, and Modernity at the Atlanta and Nashville World’s Fairs.

Nathan Cardon. A Dream of the Future: Race, Empire, and Modernity at the Atlanta and Nashville World’s Fairs.

A Dream of the Future: Race, Empire, and Modernity at the Atlanta and Nashville World's Fairs by Nathan Cardon

A Dream of the Future: Race, Empire, and Modernity at the Atlanta and Nashville World's Fairs by Nathan Cardon

Globally Optimal Vertical Direction Estimation in Atlanta World.

In man-made environments, most of the objects and structures are organized in the form of orthogonal and parallel planes. These planes can be approximated by an Atlanta world assumption, in which the normals of planes can be represented by Atlanta frames. The Atlanta world assumption has one vertical frame and multiple horizontal frames. Conventionally, given a set of inputs such as surface normals, the Atlanta frame estimation problem can be solved by a branch-and-bound (BnB) algorithm. However, the runtime of the BnB algorithm will increase greatly when the dimensionality (i.e., the number of horizontal frames) increases. In this paper, we estimate only the vertical direction, instead of all Atlanta frames at once. Accordingly, we propose a vertical direction estimation method by considering the relationship between the vertical frame and horizontal frames. Concretely, our approach employs a BnB algorithm to search the vertical direction, thereby guaranteeing global optimality without requiring prior knowledge of the number of Atlanta frames. In order to guarantee convergence, four novel bounds are investigated, by mapping a 3D hemisphere to a 2D region. We verify the feasibility of the proposed method using various challenging synthetic and real-world data.

StructVIO: Visual-Inertial Odometry With Structural Regularity of Man-Made Environments

We propose a novel visual-inertial odometry approach that adopts structural regularity in man-made environments. Instead of using Manhattan world assumption, we use Atlanta world model to describe such regularity. An Atlanta world is a world that contains multiple local Manhattan worlds with different heading directions. Each local Manhattan world is detected on-the-fly, and their headings are gradually refined by the state estimator when new observations are coming. With fully exploration of structural lines that aligned with each local Manhattan worlds, our visual-inertial odometry method become more accurate and robust, as well as much more flexible to different kinds of complex man-made environments. Through extensive benchmark tests and real-world tests, the results show that the proposed approach outperforms existing visual-inertial systems in large-scale man-made environments

Globally Optimal Inlier Set Maximization for Atlanta World Understanding.

In this work, we describe man-made structures via an appropriate structure assumption, called the Atlanta world assumption, which contains a vertical direction (typically the gravity direction) and a set of horizontal directions orthogonal to the vertical direction. Contrary to the commonly used Manhattan world assumption, the horizontal directions in Atlanta world are not necessarily orthogonal to each other. While Atlanta world can encompass a wider range of scenes, this makes the search space much larger and the problem more challenging. Our input data is a set of surface normals, for example, acquired from RGB-D cameras or 3D laser scanners, as well as lines from calibrated images. Given this input data, we propose the first globally optimal method of inlier set maximization for Atlanta direction estimation. We define a novel search space for Atlanta world, as well as its parametrization, and solve this challenging problem using a branch-and-bound (BnB) framework. To alleviate the computational bottleneck in BnB, i.e., the bound computation, we present two bound computation strategies: rectangular bound and slice bound in an efficient measurement domain, i.e., the extended Gaussian image (EGI). In addition, we propose an efficient two-stage method which automatically estimates the number of horizontal directions of a scene. Experimental results with synthetic and real-world datasets have successfully confirmed the validity of our approach.

Negro building: black Americans in the world of fairs and museums

List of Figures Acknowledgments Introduction Prologue 1. Progress of a Race: The Black Side's Contribution to Atlanta's World's Fair 2. Exhibiting the American Negro 3. Remembering Emancipation Up North 4. Look Back, March Forward 5. To Make a Black Museum Epilogue Notes Bibliography Index

Muslim and Christian: Sharing in Ministry

The following two articles witness to the impact that interfaith relations can have in transforming those involved in actually deepening their own faith. The first article describes the close relationship between a Christian pastor, Gerald Durley, and a Muslim Imam, Plemon Tauheed El-Amin. The second article is about a Christian layperson whose own faith was deepened thought the experience of observing the Muslim practice of fasting during Ramadan. Both articles were written by a Jewish woman, Carla Michalove. All four of them work closely together with Atlanta World Pilgrims.

Livin' the Blues: Memoirs of a Black Journalist and Poet.

Frank Marshall Davies (1905-1987) was a prominent African American poet and journalist in the 1930s and 1940s. Although not as familiar a name as his contemporaries, Zora Neale Hurston, Richard Wright, Gwendoline Brooks and Langston Hughes, Davis was a significant figure during the Depression and the Second World War. He wrote and published four collections of poetry in the 1930s and 1940s. Man's Verse (1935), I Am the American Negro (1937), Through Sepia Eyes (1938), and 47th Street: Poems (1948). Davis turned his back on a sustained literary career by moving to Hawaii in 1948. There he cut himself off from the busy world of Chicago writers and virtually disappeared from literary history, until interest in his work was revived in the 1960s Black Arts Movement, which hailed him as a pioneer of Black poetry and established him as a member of its canon. Because of his early self-removal from the literary limelight, Davis' life and work have been shrouded in mystery. His autobiography offers us a chance to rediscover this poet and writer and stands as an example of Black autobiography, similar in form, style and message to those of Langston Hughes and Richard Wright. In addition to his writing achievements, Davis was an editor for several African American newspapers in the 1930s; the Chicago Evening Bulletin, the Chicago Whip, the Chicago Star and the Atlanta World. In the early 1940s he began teaching what he believed to be the first history of jazz course at the Abraham Lincoln School in Chicago, and in 1945 he began broadcasting his own radio jazz show, Bronzeville Brevities on WJJD in Chicago. Active in the civil rights movement, Davis served as vice chairman of the Chicago Civil Liberties Committee from 1944 to 1947 and was a member of the national board of the Civil Rights Congress from 1947 to 1948. His autobiography chronicles the unravelling of Davis' negative self-image and suggest how he was able to construct a healthy, self-assured life. The common thread connecting the disparate events of Davis' life is the blues. By rooting itself in a blues sensibility, his life story is one of triumph over economic hardship and racial discrimination. He captures what it was like to grow up black and poor, struggling towards both economic and emotional self-sufficiency.

W. A. Scott and the Atlanta World

W. A. Scott and the Atlanta <i>World</i>