StructSLAM: Visual SLAM With Building Structure Lines

Zhou, Huizhong; Zou, Danping; Pei, Ling; Ying, Rendong; Liu, Peilin; Wang, Yu

doi:10.1109/tvt.2015.2388780

Cited by 241 publications

(118 citation statements)

References 29 publications

Supporting

Mentioning

117

Contrasting

Order By: Relevance

“…Lu et al [15] incorporated a combination of points, lines, planes, and vanishing points into bundle adjustment. Zhou et al [16] proposed a novel visual SLAM (using EKF) based on Manhattan World assumption using points and lines. Manhattan World assumption, proposed Coughlan et al [4] in 1999, states that most planar surfaces in urban scenes lie in one of three mutually orthogonal orientations.…”

Section: Related Workmentioning

confidence: 99%

Line-Based SLAM Using Non-Overlapping Cameras in an Urban Environment

Kawasaki

Hara

Saitō

2018

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYWe propose a method of line-based Simultaneous Localization and Mapping (SLAM) using non-overlapping multiple cameras for vehicles running in an urban environment. It uses corresponding line segments between images taken by different frames and different cameras. The contribution is a novel line segment matching algorithm by warping processing based on urban structures. This idea significantly improves the accuracy of line segment matching when viewing direction are very different, so that a number of correspondences between front-view and rear-view cameras can be found and the accuracy of SLAM can be improved. Additionally, to enhance the accuracy of SLAM we apply a geometrical constraint of urban area for initial estimation of 3D mapping of line segments and optimization by bundle adjustment. We can further improve the accuracy of SLAM by combining points and lines. The position error is stable within 1.5m for the entire image dataset evaluated in this paper. The estimation accuracy of our method is as high as that of ground truth captured by RTK-GPS. Our high accuracy SLAM algorithm can be apply for generating a road map represented by line segments. According to an evaluation of our generating map, true positive rate around the vehicle exceeding 70% is achieved.

show abstract

Section: Related Workmentioning

confidence: 99%

Line-Based SLAM Using Non-Overlapping Cameras in an Urban Environment

Kawasaki

Hara

Saitō

2018

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

show abstract

“…Currently, there are two main approaches to implementing vision-based SLAM systems: i) filtering-based methods (see [20][21][22] and [23]), and ii) optimization-based methods (see [24] and [25]). While the former approach is shown to give accurate results when the availability of computational power is enough, filtering-based SLAM methods might still be beneficial if limited processing power is available [26].…”

Section: Related Workmentioning

confidence: 99%

A Visual-Aided Inertial Navigation and Mapping System

Munguía

Nuño

Aldana

et al. 2016

International Journal of Advanced Robotic Systems

View full text Add to dashboard Cite

State estimation is a fundamental necessity for any application involving autonomous robots. This paper describes a visual-aided inertial navigation and mapping system for application to autonomous robots. The system, which relies on Kalman filtering, is designed to fuse the measurements obtained from a monocular camera, an inertial measurement unit (IMU) and a position sensor (GPS). The estimated state consists of the full state of the vehicle: the position, orientation, their first derivatives and the parameter errors of the inertial sensors (i.e., the bias of gyroscopes and accelerometers). The system also provides the spatial locations of the visual features observed by the camera. The proposed scheme was designed by considering the limited resources commonly available in small mobile robots, while it is intended to be applied to cluttered environments in order to perform fully vision-based navigation in periods where the position sensor is not available. Moreover, the estimated map of visual features would be suitable for multiple tasks: i) terrain analysis; ii) three-dimensional (3D) scene reconstruction; iii) localization, detection or perception of obstacles and generating trajectories to navigate around these obstacles; and iv) autonomous exploration. In this work, simulations and experiments with real data are presented in order to validate and demonstrate the performance of the proposal.

show abstract

“…However, these results are often inappropriate in those applications which need a fast analysis as well as a quick availability of data of interest. These problems are solved in [31] where a 6-degree-of-freedom (DoF) V-SLAM method is developed to find the structural regularities, i.e., lines, typical of man-made buildings to determine the global position of the vehicles. However, this approach still suffers from classical problems of passive methods which are highly dependent on the ambient lighting conditions.…”

Section: A Related Workmentioning

confidence: 99%

An Embedded Vision System for Real-Time Autonomous Localization Using Laser Profilometry

Patruno

Marani

Nitti

et al. 2015

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

In this paper, we propose an embedded vision system based on laser profilometry able to get the pose of a vehicle and its relative displacements with reference to the constitutive media of a structured environment. Fundamental equations for laser triangulation are developed and encoded for their actual implementation on an embedded system. It is made of a laser source that projects a line-shaped beam onto the environment and an on-chip camera able to frame the laser light. Images are then sent to the inexpensive Raspberry Pi onboard computer, which is responsible for processing tasks. For the first time, laser profilometry is coupled with the correlation of laser signatures on a low-cost and low-resource processing board for vehicle localization purposes. Several validation tests of the proposed sensor have proven the effectiveness of the system with respect to commercially available sensors such as inductive sensors and standard odometers, which fail when the vehicle crosses path interceptions or its wheels undergo unavoidable slippages. Moreover, further comparisons with other vision-based techniques have also proven the good performances of this embedded system for real-time localization of vehicles.Index Terms-Visual odometry, image processing, laser profilometry, indoor autonomous vehicle navigation, real-time embedded systems. 1524-9050

show abstract

StructSLAM: Visual SLAM With Building Structure Lines

Cited by 241 publications

References 29 publications

Line-Based SLAM Using Non-Overlapping Cameras in an Urban Environment

Line-Based SLAM Using Non-Overlapping Cameras in an Urban Environment

A Visual-Aided Inertial Navigation and Mapping System

An Embedded Vision System for Real-Time Autonomous Localization Using Laser Profilometry

Contact Info

Product

Resources

About