A two-tier map representation for compact-stereo-vision-based SLAM

Robotica ◽  
2011 ◽  
Vol 30 (2) ◽  
pp. 245-256 ◽  
Author(s):  
D. C. Herath ◽  
S. Kodagoda ◽  
G. Dissanayake
Keyword(s):  
Stereo Vision ◽  
Mathematical Framework ◽  
Localization And Mapping ◽  
Vision Sensors ◽  
Highly Nonlinear ◽  
Novel Method ◽  
Small Baseline ◽  
State Estimations ◽  
Feature Management ◽  
Recursive Estimators

SUMMARYVision sensors are increasingly being used in the implementation of Simultaneous Localization and Mapping (SLAM). Even though the mathematical framework of SLAM is well understood, considerable issues remain to be resolved when a particular sensing modality is considered. For instance, the observation model of a small baseline stereo camera is known to be highly nonlinear. As a consequence, state estimations obtained from standard recursive estimators, such as the Extended Kalman Filter, tend to be inconsistent. Further, vision-based approaches are plagued with high feature densities, and the consequent requisite of maintaining large feature databases for loop closure and data association. This paper proposes a two-tier solution for resolving these issues, inspired by the mechanics of human navigation. The proposed two-tier solution addresses the consistency issue by formulating the SLAM problem as a nonlinear batch optimization and presents a novel method for feature management through a two-tier map representation. Simulations and experiments are carried out in an office-like environment to validate the performance of the algorithm.

scholarly journals Composite clustering normal distribution transform algorithm

2020 ◽  
Vol 17 (3) ◽  
pp. 172988142091214
Author(s):  
Tian Liu ◽  
Jiongzhi Zheng ◽  
Zhenting Wang ◽  
Zhengdong Huang ◽  
Yongfu Chen
Keyword(s):  
Normal Distribution ◽  
Experimental Result ◽  
Geometric Feature ◽  
Matching Accuracy ◽  
Localization And Mapping ◽  
Density Based Clustering ◽  
Scan Registration ◽  
Novel Method ◽  
Value Decomposition

Scan registration is a fundamental step for the simultaneous localization and mapping of mobile robot. The accuracy of scan registration is critical for the quality of mapping and the accuracy of robot navigation. During all of the scan registration methods, normal distribution transform is an efficient and wild-using one. But normal distribution transform will lead to the unreasonable interruption when splitting the grid and can’t express the points’ local geometric feature by prefixed grid. In this article, we propose a novel method, composite clustering normal distribution transform, which comprises the density-based clustering and k-means clustering to aggregate the points with similar local distributing feature. It takes singular value decomposition to judge the suitable degree of one cluster for further division. Meanwhile, to avoid the radiating phenomenon of LIDAR in measuring the points’ distance, we propose a method based on trigonometric to measure the internal distance. The clustering method in composite clustering normal distribution transform could ensure the expression of LIDAR’s local distribution and matching accuracy. The experimental result demonstrates that our method is more accurate and more stable than the normal distribution transform and iterative closest point methods.

scholarly journals Application of a Real-Time Visualization Method of AUVs in Underwater Visual Localization

2019 ◽  
Vol 9 (7) ◽  
pp. 1428 ◽  
Author(s):  
Ran Wang ◽  
Xin Wang ◽  
MingMing Zhu ◽  
YinFu Lin
Keyword(s):  
Autonomous Underwater Vehicles ◽  
Underwater Vehicles ◽  
Visual Localization ◽  
Visualization Method ◽  
Localization And Mapping ◽  
Real Time Visualization ◽  
Novel Method ◽  
Vision Based Localization ◽  
Slam Algorithm ◽  
Localization Systems

Autonomous underwater vehicles (AUVs) are widely used, but it is a tough challenge to guarantee the underwater location accuracy of AUVs. In this paper, a novel method is proposed to improve the accuracy of vision-based localization systems in feature-poor underwater environments. The traditional stereo visual simultaneous localization and mapping (SLAM) algorithm, which relies on the detection of tracking features, is used to estimate the position of the camera and establish a map of the environment. However, it is hard to find enough reliable point features in underwater environments and thus the performance of the algorithm is reduced. A stereo point and line SLAM (PL-SLAM) algorithm for localization, which utilizes point and line information simultaneously, was investigated in this study to resolve the problem. Experiments with an AR-marker (Augmented Reality-marker) were carried out to validate the accuracy and effect of the investigated algorithm.

A Method for Constructing Bijective S-Box with High Nonlinearity Based on Chaos and Optimization

2015 ◽  
Vol 25 (10) ◽  
pp. 1550127 ◽  
Author(s):  
Yong Wang ◽  
Peng Lei ◽  
Kwok-Wo Wong
Keyword(s):  
Chaotic Maps ◽  
Chaotic Map ◽  
Optimization Method ◽  
Second Phase ◽  
Local Optima ◽  
High Nonlinearity ◽  
Highly Nonlinear ◽  
Novel Method ◽  
High Level ◽  
Very High

Although chaotic maps possess useful properties, such as being highly nonlinear and pseudorandom, for designing S-box, the cryptographic performance of the chaos-based substitution box (S-box) cannot achieve a very high level, especially in nonlinearity. In this paper, two conditions of improving the nonlinearity of S-box are firstly given according to the process of calculating nonlinearity. A novel method combining chaos and optimization operations is proposed for constructing S-box with high nonlinearity. There are three phases in our method. In the first phase, the S-box is initialized by a chaotic map. Then, its nonlinearity is enhanced by an optimization method in the second phase. To avoid the result of falling into local optima, some adjustments are done in the final phase. Experimental results show that the S-boxes constructed by the proposed method have a much higher nonlinearity than those only based on chaotic maps. This justifies that our algorithm is effective in generating S-boxes with high cryptographic performance.

scholarly journals Bridge Inspection Using Unmanned Aerial Vehicle Based on HG-SLAM: Hierarchical Graph-Based SLAM

2020 ◽  
Vol 12 (18) ◽  
pp. 3022
Author(s):  
Sungwook Jung ◽  
Duckyu Choi ◽  
Seungwon Song ◽  
Hyun Myung
Keyword(s):  
Autonomous Systems ◽  
Stable Node ◽  
Bridge Inspection ◽  
Hierarchical Graph ◽  
Localization And Mapping ◽  
Human Labor ◽  
Aerial Vehicle ◽  
Graph Slam ◽  
Point G ◽  
State Estimations

With the increasing demand for autonomous systems in the field of inspection, the use of unmanned aerial vehicles (UAVs) to replace human labor is becoming more frequent. However, the Global Positioning System (GPS) signal is usually denied in environments near or under bridges, which makes the manual operation of a UAV difficult and unreliable in these areas. This paper addresses a novel hierarchical graph-based simultaneous localization and mapping (SLAM) method for fully autonomous bridge inspection using an aerial vehicle, as well as a technical method for UAV control for actually conducting bridge inspections. Due to the harsh environment involved and the corresponding limitations on GPS usage, a graph-based SLAM approach using a tilted 3D LiDAR (Light Detection and Ranging) and a monocular camera to localize the UAV and map the target bridge is proposed. Each visual-inertial state estimate and the corresponding LiDAR sweep are combined into a single subnode. These subnodes make up a “supernode” that consists of state estimations and accumulated scan data for robust and stable node generation in graph SLAM. The constraints are generated from LiDAR data using the normal distribution transform (NDT) and generalized iterative closest point (G-ICP) matching. The feasibility of the proposed method was verified on two different types of bridges: on the ground and offshore.

Cooperative and asynchronous stereo vision for dynamic vision sensors

2014 ◽  
Vol 25 (5) ◽  
pp. 055108 ◽  
Author(s):  
E Piatkowska ◽  
A N Belbachir ◽  
M Gelautz
Keyword(s):  
Stereo Vision ◽  
Vision Sensors ◽  
Dynamic Vision
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