scholarly journals Computationally Efficient Cooperative Dynamic Range-Only SLAM Based on Sum of Gaussian Filter

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3306
Author(s):  
Jung-Hee Kim ◽  
Doik Kim
Keyword(s):  
Convergence Rate ◽  
Computational Analysis ◽  
Dynamic Range ◽  
Estimation Error ◽  
Dynamic Environments ◽  
Map Estimation ◽  
Computationally Efficient ◽  
Localization Accuracy ◽  
Localization And Mapping ◽  
Slam Algorithm

A cooperative dynamic range-only simultaneous localization and mapping (CDRO-SLAM) algorithm based on the sum of Gaussian (SoG) filter was recently introduced. The main characteristics of the CDRO-SLAM are (i) the integration of inter-node ranges as well as usual direct robot-node ranges to improve the convergence rate and localization accuracy and (ii) the tracking of any moving nodes under dynamic environments by resetting and updating the SoG variables. In this paper, an efficient implementation of the CDRO-SLAM (eCDRO-SLAM) is proposed to mitigate the high computational burden of the CDRO-SLAM due to the inter-node measurements. Furthermore, a thorough computational analysis is presented, which reveals that the computational efficiency of the eCDRO-SLAM is significantly improved over the CDRO-SLAM. The performance of the proposed eCDRO-SLAM is compared with those of several conventional RO-SLAM algorithms and the results show that the proposed efficient algorithm has a faster convergence rate and a similar map estimation error regardless of the map size. Accordingly, the proposed eCDRO-SLAM can be utilized in various RO-SLAM applications.

scholarly journals An Adaptive UKF Based SLAM Method for Unmanned Underwater Vehicle

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Hongjian Wang ◽  
Guixia Fu ◽  
Juan Li ◽  
Zheping Yan ◽  
Xinqian Bian
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Estimation Error ◽  
Underwater Vehicle ◽  
Statistical Parameters ◽  
Sea Trial ◽  
Unmanned Underwater Vehicle ◽  
Noise Statistic ◽  
Localization And Mapping ◽  
Slam Algorithm

This work proposes an improved unscented Kalman filter (UKF)-based simultaneous localization and mapping (SLAM) algorithm based on an adaptive unscented Kalman filter (AUKF) with a noise statistic estimator. The algorithm solves the issue that conventional UKF-SLAM algorithms have declining accuracy, with divergence occurring when the prior noise statistic is unknown and time-varying. The new SLAM algorithm performs an online estimation of the statistical parameters of unknown system noise by introducing a modified Sage-Husa noise statistic estimator. The algorithm also judges whether the filter is divergent and restrains potential filtering divergence using a covariance matching method. This approach reduces state estimation error, effectively improving navigation accuracy of the SLAM system. A line feature extraction is implemented through a Hough transform based on the ranging sonar model. Test results based on unmanned underwater vehicle (UUV) sea trial data indicate that the proposed AUKF-SLAM algorithm is valid and feasible and provides better accuracy than the standard UKF-SLAM system.

scholarly journals ON CONSTRUCTION OF A RELIABLE GROUND TRUTH FOR EVALUATION OF VISUAL SLAM ALGORITHMS

2016 ◽  
Vol 6 ◽  
pp. 1-5
Author(s):  
Jan Bayer ◽  
Petr Čížek ◽  
Jan Faigl
Keyword(s):  
Ground Truth ◽  
Accuracy Evaluation ◽  
Algorithm Performance ◽  
Localization Accuracy ◽  
Trajectory Error ◽  
Localization System ◽  
Localization And Mapping ◽  
On Line ◽  
Slam Algorithm ◽  
Frame Processing

In this work we are concerning the problem of localization accuracy evaluation of visual-based Simultaneous Localization and Mapping (SLAM) techniques. Quantitative evaluation of the SLAM algorithm performance is usually done using the established metrics of Relative pose error and Absolute trajectory error which require a precise and reliable ground truth. Such a ground truth is usually hard to obtain, while it requires an expensive external localization system. In this work we are proposing to use the SLAM algorithm itself to construct a reliable ground-truth by offline frame-by-frame processing. The generated ground-truth is suitable for evaluation of different SLAM systems, as well as for tuning the parametrization of the on-line SLAM. The presented practical experimental results indicate the feasibility of the proposed approach.

Comparison of different SLAM approaches for a driverless race car

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Nick Le Large ◽  
Frank Bieder ◽  
Martin Lauer
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Ground Truth ◽  
Time Constraints ◽  
Differential Gps ◽  
Real World Data ◽  
Race Car ◽  
Localization And Mapping ◽  
Slam Algorithm ◽  
Computational Resources

Abstract For the application of an automated, driverless race car, we aim to assure high map and localization quality for successful driving on previously unknown, narrow race tracks. To achieve this goal, it is essential to choose an algorithm that fulfills the requirements in terms of accuracy, computational resources and run time. We propose both a filter-based and a smoothing-based Simultaneous Localization and Mapping (SLAM) algorithm and evaluate them using real-world data collected by a Formula Student Driverless race car. The accuracy is measured by comparing the SLAM-generated map to a ground truth map which was acquired using high-precision Differential GPS (DGPS) measurements. The results of the evaluation show that both algorithms meet required time constraints thanks to a parallelized architecture, with GraphSLAM draining the computational resources much faster than Extended Kalman Filter (EKF) SLAM. However, the analysis of the maps generated by the algorithms shows that GraphSLAM outperforms EKF SLAM in terms of accuracy.

scholarly journals A survey: which features are required for dynamic visual simultaneous localization and mapping?

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Zewen Xu ◽  
Zheng Rong ◽  
Yihong Wu
Keyword(s):  
Simultaneous Localization And Mapping ◽  
Dynamic Environments ◽  
Visual Features ◽  
Advantages And Disadvantages ◽  
Intelligent Robots ◽  
Localization And Mapping ◽  
High Level ◽  
Static World ◽  
Robotic Applications ◽  
Significant Attention

AbstractIn recent years, simultaneous localization and mapping in dynamic environments (dynamic SLAM) has attracted significant attention from both academia and industry. Some pioneering work on this technique has expanded the potential of robotic applications. Compared to standard SLAM under the static world assumption, dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly. Therefore, dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments. Additionally, to meet the demands of some high-level tasks, dynamic SLAM can be integrated with multiple object tracking. This article presents a survey on dynamic SLAM from the perspective of feature choices. A discussion of the advantages and disadvantages of different visual features is provided in this article.

scholarly journals A V-SLAM Guided and Portable System for Photogrammetric Applications

2021 ◽  
Vol 13 (12) ◽  
pp. 2351
Author(s):  
Alessandro Torresani ◽  
Fabio Menna ◽  
Roberto Battisti ◽  
Fabio Remondino
Keyword(s):  
Low Cost ◽  
Cost Effective ◽  
Motion Blur ◽  
Guidance System ◽  
Prototype System ◽  
Active Sensors ◽  
Localization And Mapping ◽  
Acquisition Speed ◽  
Mobile Mapping System ◽  
Slam Algorithm

Mobile and handheld mapping systems are becoming widely used nowadays as fast and cost-effective data acquisition systems for 3D reconstruction purposes. While most of the research and commercial systems are based on active sensors, solutions employing only cameras and photogrammetry are attracting more and more interest due to their significantly minor costs, size and power consumption. In this work we propose an ARM-based, low-cost and lightweight stereo vision mobile mapping system based on a Visual Simultaneous Localization And Mapping (V-SLAM) algorithm. The prototype system, named GuPho (Guided Photogrammetric System) also integrates an in-house guidance system which enables optimized image acquisitions, robust management of the cameras and feedback on positioning and acquisition speed. The presented results show the effectiveness of the developed prototype in mapping large scenarios, enabling motion blur prevention, robust camera exposure control and achieving accurate 3D results.

scholarly journals An Improved Localization Method for the Transition between Autonomous Underwater Vehicle Homing and Docking

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2468
Author(s):  
Ri Lin ◽  
Feng Zhang ◽  
Dejun Li ◽  
Mingwei Lin ◽  
Gengli Zhou ◽  
...  
Keyword(s):  
Data Exchange ◽  
Autonomous Underwater Vehicle ◽  
Autonomous Underwater Vehicles ◽  
Optimization Method ◽  
Short Baseline ◽  
Localization Accuracy ◽  
Localization Method ◽  
Mapping Algorithm ◽  
Localization And Mapping ◽  
Update Rate

Docking technology for autonomous underwater vehicles (AUVs) involves energy supply, data exchange and navigation, and plays an important role to extend the endurance of the AUVs. The navigation method used in the transition between AUV homing and docking influences subsequent tasks. How to improve the accuracy of the navigation in this stage is important. However, when using ultra-short baseline (USBL), outliers and slow localization updating rates could possibly cause localization errors. Optical navigation methods using underwater lights and cameras are easily affected by the ambient light. All these may reduce the rate of successful docking. In this paper, research on an improved localization method based on multi-sensor information fusion is carried out. To improve the localization performance of AUVs under motion mutation and light variation conditions, an improved underwater simultaneous localization and mapping algorithm based on ORB features (IU-ORBSALM) is proposed. A nonlinear optimization method is proposed to optimize the scale of monocular visual odometry in IU-ORBSLAM and the AUV pose. Localization tests and five docking missions are executed in a swimming pool. The localization results indicate that the localization accuracy and update rate are both improved. The 100% successful docking rate achieved verifies the feasibility of the proposed localization method.

scholarly journals Sensor Fusion-Based Approach to Eliminating Moving Objects for SLAM in Dynamic Environments

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 230
Author(s):  
Xiangwei Dang ◽  
Zheng Rong ◽  
Xingdong Liang
Keyword(s):  
State Estimation ◽  
Autonomous Navigation ◽  
Moving Objects ◽  
Physical Simulation ◽  
Point Clouds ◽  
Dynamic Environments ◽  
Accurate Localization ◽  
Novel Approach ◽  
Localization And Mapping ◽  
Real World Datasets

Accurate localization and reliable mapping is essential for autonomous navigation of robots. As one of the core technologies for autonomous navigation, Simultaneous Localization and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a robot’s state estimation. However, most of the mature SLAM methods generally work under the assumption that the environment is static, while in dynamic environments they will yield degenerate performance or even fail. In this paper, first we quantitatively evaluate the performance of the state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the environment. Through semi-physical simulation, we observed that the shape, size, and distribution of moving objects all can impact the performance of SLAM significantly, and obtained instructive investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based on the above investigation, a novel approach named EMO to eliminating the moving objects for SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness of state estimation. The method fully uses the advantages of different characteristics of two sensors to realize the fusion of sensor information with two different resolutions. The moving objects can be efficiently detected based on Doppler effect by radar, accurately segmented and localized by LiDAR, then filtered out from the point clouds through data association and accurate synchronized in time and space. Finally, the point clouds representing the static environment are used as the input of SLAM. The proposed approach is evaluated through experiments using both semi-physical simulation and real-world datasets. The results demonstrate the effectiveness of the method at improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and robustness in dynamic environments.

scholarly journals Pedestrian Navigation System with Trinal-IMUs for Drastic Motions

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5570
Author(s):  
Yiming Ding ◽  
Zhi Xiong ◽  
Wanling Li ◽  
Zhiguo Cao ◽  
Zhengchun Wang
Keyword(s):  
Navigation System ◽  
Inertial Sensor ◽  
Estimation Error ◽  
Simultaneous Localization And Mapping ◽  
Step Length ◽  
Mapping Method ◽  
Zero Crossing ◽  
Pedestrian Navigation ◽  
Length Estimation ◽  
Localization And Mapping

The combination of biomechanics and inertial pedestrian navigation research provides a very promising approach for pedestrian positioning in environments where Global Positioning System (GPS) signal is unavailable. However, in practical applications such as fire rescue and indoor security, the inertial sensor-based pedestrian navigation system is facing various challenges, especially the step length estimation errors and heading drift in running and sprint. In this paper, a trinal-node, including two thigh-worn inertial measurement units (IMU) and one waist-worn IMU, based simultaneous localization and occupation grid mapping method is proposed. Specifically, the gait detection and segmentation are realized by the zero-crossing detection of the difference of thighs pitch angle. A piecewise function between the step length and the probability distribution of waist horizontal acceleration is established to achieve accurate step length estimation both in regular walking and drastic motions. In addition, the simultaneous localization and mapping method based on occupancy grids, which involves the historic trajectory to improve the pedestrian’s pose estimation is introduced. The experiments show that the proposed trinal-node pedestrian inertial odometer can identify and segment each gait cycle in the walking, running, and sprint. The average step length estimation error is no more than 3.58% of the total travel distance in the motion speed from 1.23 m/s to 3.92 m/s. In combination with the proposed simultaneous localization and mapping method based on the occupancy grid, the localization error is less than 5 m in a single-story building of 2643.2 m2.

scholarly journals Multi-Focal Vision and Gaze Control Improve Navigation Performance

10.5772/50920 ◽  
2012 ◽  
Vol 9 (1) ◽  
pp. 25 ◽  
Author(s):  
Kolja Kühnlenz ◽  
Martin Buss
Keyword(s):  
Mobile Robot ◽  
Robot Navigation ◽  
Active Vision ◽  
Path Following ◽  
Mobile Robot Navigation ◽  
Vision Systems ◽  
Localization Accuracy ◽  
Localization And Mapping ◽  
Novel Concept ◽  
The Impact

Multi-focal vision systems comprise cameras with various fields of view and measurement accuracies. This article presents a multi-focal approach to localization and mapping of mobile robots with active vision. An implementation of the novel concept is done considering a humanoid robot navigation scenario where the robot is visually guided through a structured environment with several landmarks. Various embodiments of multi-focal vision systems are investigated and the impact on navigation performance is evaluated in comparison to a conventional mono-focal stereo set-up. The comparative studies clearly show the benefits of multi-focal vision for mobile robot navigation: flexibility to assign the different available sensors optimally in each situation, enhancement of the visible field, higher localization accuracy, and, thus, better task performance, i.e. path following behavior of the mobile robot. It is shown that multi-focal vision may strongly improve navigation performance.

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