Semantic Visual Localization and Mapping in the Parking Lot Using Direct Method

2021 ◽  
Author(s):  
Tengfei Huang ◽  
Zhihong Chen ◽  
Junqiao Zhao ◽  
Jiafeng Cui ◽  
Xuebo Tian ◽  
...  
Keyword(s):  
Direct Method ◽  
Visual Localization ◽  
Localization And Mapping ◽  
Parking Lot

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.

scholarly journals A Visual SLAM Robust against Dynamic Objects Based on Hybrid Semantic-Geometry Information

2021 ◽  
Vol 10 (10) ◽  
pp. 673
Author(s):  
Sheng Miao ◽  
Xiaoxiong Liu ◽  
Dazheng Wei ◽  
Changze Li
Keyword(s):  
Moving Objects ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Visual Localization ◽  
Epipolar Constraint ◽  
Trajectory Error ◽  
Localization And Mapping ◽  
Dynamic Objects ◽  
Public Datasets ◽  
Localization Approach

A visual localization approach for dynamic objects based on hybrid semantic-geometry information is presented. Due to the interference of moving objects in the real environment, the traditional simultaneous localization and mapping (SLAM) system can be corrupted. To address this problem, we propose a method for static/dynamic image segmentation that leverages semantic and geometric modules, including optical flow residual clustering, epipolar constraint checks, semantic segmentation, and outlier elimination. We integrated the proposed approach into the state-of-the-art ORB-SLAM2 and evaluated its performance on both public datasets and a quadcopter platform. Experimental results demonstrated that the root-mean-square error of the absolute trajectory error improved, on average, by 93.63% in highly dynamic benchmarks when compared with ORB-SLAM2. Thus, the proposed method can improve the performance of state-of-the-art SLAM systems in challenging scenarios.

scholarly journals Performance Analysis of Direct GPS Spoofing Detection Method with AHRS/Accelerometer

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 954 ◽  
Author(s):  
Keum-Cheol Kwon ◽  
Duk-Sun Shim
Keyword(s):  
Direct Method ◽  
False Alarm Probability ◽  
Detection Performance ◽  
Decision Variable ◽  
The North ◽  
Localization And Mapping ◽  
Decision Variables ◽  
Spoofing Detection ◽  
East Component ◽  
The Difference

The global positioning system (GPS) is an essential technology that provides positioning capabilities and is used in various applications such as navigation, surveying, mapping, robot simultaneous localization and mapping (SLAM), location-based service (LBS), etc. However, the GPS is known to be vulnerable to intentional attacks such as spoofing because of its simple signal structure. In this study, a direct method is proposed for GPS spoofing detection, using Attitude and Heading Reference System (AHRS) accelerometer and analyzing the detection performance with corresponding probability density functions (PDFs). The difference in the acceleration between the GPS receiver and the accelerometer is used to detect spoofing. The magnitude of the acceleration error may be used as a decision variable. Additionally, using the magnitude of the north (or east) component of the acceleration error as another decision variable is proposed, which shows better performance in some conditions. The performance of the two decision variables is compared by calculating the probability of spoofing detection and the detectable minimum spoofing acceleration (DMSA), given a pre-defined false alarm probability and a pre-defined detection probability. It turns out that both decision variables need to be used together to obtain the best spoofing detection performance.

Multi-View and Multi-Scale Localization for Intelligent Vehicles in Underground Parking Lots

2019 ◽  
Vol 2673 (11) ◽  
pp. 791-800
Author(s):  
Gang Huang ◽  
Zhaozheng Hu ◽  
Mengchao Mu ◽  
Xianglong Wang ◽  
Fan Zhang
Keyword(s):  
Intelligent Vehicles ◽  
Visual Localization ◽  
Fingerprint Matching ◽  
Vehicle Localization ◽  
Localization Method ◽  
Parking Lots ◽  
Single View ◽  
Multi Scale ◽  
Parking Lot ◽  
Wifi Localization

Because of limited access to global positioning system (GPS) signals, accurate and reliable localization for intelligent vehicles in underground parking lots is still an open problem. This paper proposes a multi-view and multi-scale localization method aiming at solving this problem. The proposed method is divided into an offline mapping stage and an online localization stage. In the mapping stage, the offline map is generated by fusing 3-D information, WiFi features, visual features, and trajectory from visual odometry (VO). In the localization stage, WiFi fingerprint matching is exploited for coarse localization. Based on the result of coarse localization, multi-view localization is exploited for image-level localization. Finally, metric localization is exploited to refine the localization results. By applying this multi-scale strategy, it is possible to fuse WiFi localization and visual localization and reduce the image matching and error rate to a great extent. Because of exploiting more information, multi-view localization is more robust and accurate than single-view localization. The method is tested in a 2,000 m2 underground parking lot. The result demonstrates that this method can achieve sub-meter localization on average. The proposed localization method can be a supplement to the existing intelligent vehicle localization techniques.

GENETIC ALGORITHM BASED VISUAL LOCALIZATION FOR A ROBOT PET IN HOME HEALTHCARE SYSTEM

2007 ◽  
Vol 04 (02) ◽  
pp. 141-160 ◽  
Author(s):  
FUNG-LING TONG ◽  
MAX Q.-H. MENG
Keyword(s):  
Genetic Algorithm ◽  
Home Healthcare ◽  
Sensor Data ◽  
Mapping Technique ◽  
Depth Information ◽  
Visual Localization ◽  
Legged Robot ◽  
Localization Algorithm ◽  
Localization Algorithms ◽  
Localization And Mapping

The simultaneous localization and mapping technique is an important requirement in the development autonomous robot. Many localization algorithms for wheeled robots using various sensors have been proposed. In this article, we present a visual localization algorithm for a small home-use robot pet (legged robot). A low-resolution camera is equipped on the robot as the only sensor for localization. Challenges of visual localization for legged robots include: (1) Unmodeled motion errors due to leg slippages are common in legged robot, (2) as the oscillated walking motion of robot leads to fluctuated sensor data, the high degree of freedom of legged robot increases the complexity of the localization problem and (3) camera has limited field of view and image points lack of depth information. In the proposed algorithm, the localization for high-dimensional movement robot is modeled as an optimization. The objective function is then solved by a genetic algorithm. Approaches to (1) increase the efficiency of the search and (2) weaken the influence of noisy feature points to the localization results are presented. Results from simulations show that the proposed algorithm is able to localize the legged robot accurately and efficiently.

Characterizing Visual Localization and Mapping Datasets

2019 ◽  
Author(s):  
Sajad Saeedi ◽  
Eduardo D C Carvalho ◽  
Wenbin Li ◽  
Dimos Tzoumanikas ◽  
Stefan Leutenegger ◽  
...  
Keyword(s):  
Visual Localization ◽  
Localization And Mapping

scholarly journals Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking

Sensors ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 161 ◽  
Author(s):  
Junqiao Zhao ◽  
Yewei Huang ◽  
Xudong He ◽  
Shaoming Zhang ◽  
Chen Ye ◽  
...  
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Indoor Localization ◽  
Autonomous Driving ◽  
Human Beings ◽  
Low Level ◽  
Localization And Mapping ◽  
Challenging Tasks ◽  
Parking Lot ◽  
Feature Based

Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.

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