scholarly journals Robust Localization of the Mobile Robot Driven by Lidar Measurement and Matching for Ongoing Scene

2020 ◽  
Vol 10 (18) ◽  
pp. 6152 ◽  
Author(s):  
Zhen Xu ◽  
Shuai Guo ◽  
Tao Song ◽  
Lingdong Zeng
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
Lidar Measurement ◽  
Localization Problem ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Adaptive Weights ◽  
Robust Localization ◽  
Hybrid Localization ◽  
Matching Efficiency

Aiming at the localization problem of mobile robot in construction scenes, a hybrid localization algorithm with the adaptive weights is proposed, which can effectively improve the robust localization of mobile robot. Firstly, two indicators of localization accuracy and calculation efficiency are set to reflect the robustness of localization. Secondly, the construction scene is defined as an ongoing scene, and the robust localization of mobile robot is achieved by using the measurement of artificial landmarks and matching based on generated features. Finally, the experimental results show that the accuracy of localization is up to 8.22 mm and the most matching efficiency is controlled within 0.027 s. The hybrid localization algorithm that based on adaptive weights can realize a good robustness for tasks such as autonomous navigation and path planning in construction scenes.

scholarly journals A Hybrid Localization Approach in 3D Wireless Sensor Network

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Baohui Zhang ◽  
Jin Fan ◽  
Guojun Dai ◽  
Tom H. Luan
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Propagation Model ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Anchor Node ◽  
Localization Accuracy ◽  
3D Localization ◽  
Hybrid Localization ◽  
Range Free

Location information acquisition is crucial for many wireless sensor network (WSN) applications. While existing localization approaches mainly focus on 2D plane, the emerging 3D localization brings WSNs closer to reality with much enhanced accuracy. Two types of 3D localization algorithms are mainly used in localization application: the range-based localization and the range-free localization. The range-based localization algorithm has strict requirements on hardware and therefore is costly to implement in practice. The range-free localization algorithm reduces the hardware cost but at the expense of low localization accuracy. On addressing the shortage of both algorithms, in this paper, we develop a novel hybrid localization scheme, which utilizes the range-based attribute RSSI and the range-free attribute hopsize, to achieve accurate yet low-cost 3D localization. As anchor node deployment strategy plays an important role in improving the localization accuracy, an anchor node configuration scheme is also developed in this work by utilizing the MIS (maximal independent set) of a network. With proper anchor node configuration and propagation model selection, using simulations, we show that our proposed algorithm improves the localization accuracy by 38.9% compared with 3D DV-HOP and 52.7% compared with 3D centroid.

Mobile robot motion control and autonomous navigation in GPS-denied outdoor environments using 3D laser scanning

2019 ◽  
Vol 39 (3) ◽  
pp. 469-478
Author(s):  
Qifeng Yang ◽  
Daokui Qu ◽  
Fang Xu ◽  
Fengshan Zou ◽  
Guojian He ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Motion Control ◽  
Autonomous Navigation ◽  
Laser Scanning ◽  
Large Scale ◽  
Laser Scanner ◽  
Geometric Feature ◽  
Content Type ◽  
Localization Accuracy ◽  
Novel Approach

Purpose This paper aims to propose a series of approaches to solve the problem of the mobile robot motion control and autonomous navigation in large-scale outdoor GPS-denied environments. Design/methodology/approach Based on the model of mobile robot with two driving wheels, a controller is designed and tested in obstacle-cluttered scenes in this paper. By using the priori “topology-geometry” map constructed based on the odometer data and the online matching algorithm of 3D-laser scanning points, a novel approach of outdoor localization with 3D-laser scanner is proposed to solve the problem of poor localization accuracy in GPS-denied environments. A path planning strategy based on geometric feature analysis and priority evaluation algorithm is also adopted to ensure the safety and reliability of mobile robot’s autonomous navigation and control. Findings A series of experiments are conducted with a self-designed mobile robot platform in large-scale outdoor environments, and the experimental results show the validity and effectiveness of the proposed approach. Originality/value The problem of motion control for a differential drive mobile robot is investigated in this paper first. At the same time, a novel approach of outdoor localization with 3D-laser scanner is proposed to solve the problem of poor localization accuracy in GPS-denied environments. A path planning strategy based on geometric feature analysis and priority evaluation algorithm is also adopted to ensure the safety and reliability of mobile robot’s autonomous navigation and control.

scholarly journals Adaptive Fading Extended Kalman Filtering for Mobile Robot Localization Using a Doppler–Azimuth Radar

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2544
Author(s):  
Bin Li ◽  
Yanyang Lu ◽  
Hamid Reza Karimi
Keyword(s):  
Mobile Robot ◽  
Kalman Filtering ◽  
Kinematic Model ◽  
Robot Localization ◽  
Localization Problem ◽  
Localization Algorithm ◽  
Extended Kalman Filtering ◽  
Localization Scheme ◽  
Modeling Errors ◽  
The Impact

In this paper, the localization problem of a mobile robot equipped with a Doppler–azimuth radar (D–AR) is investigated in the environment with multiple landmarks. For the type (2,0) robot kinematic model, the unknown modeling errors are generally aroused by the inaccurate odometer measurement. Meanwhile, the inaccurate odometer measurement can also give rise to a type of unknown bias for the D–AR measurement. For reducing the influence induced by modeling errors on the localization performance and enhancing the practicability of the developed robot localization algorithm, an adaptive fading extended Kalman filter (AFEKF)-based robot localization scheme is proposed. First, the robot kinematic model and the D–AR measurement model are modified by considering the impact caused by the inaccurate odometer measurement. Subsequently, in the frame of adaptive fading extended Kalman filtering, the way to the addressed robot localization problem with unknown biases is sought out and the stability of the developed AFEKF-based localization algorithm is also discussed. Finally, in order to testify the feasibility of the AFEKF-based localization scheme, three different kinds of modeling errors are considered and the comparative simulations are conducted with the conventional EKF. From the comparative simulation results, it can be seen that the average localization error under the developed AFEKF-based localization scheme is [0.0245m0.0224m0.0039rad]T and the average localization errors using the conventional EKF are [1.0405m2.2700m0.1782rad]T, [0.4963m0.3482m0.0254rad]T and [0.2774m0.3897m0.0353rad]T, respectively, under the three cases of the constant bias, the white Gaussian stochastic bias and the bounded uncertainty bias.

A Study of Sensor-Fusion Mechanism for Mobile Robot Global Localization

Robotica ◽  
2019 ◽  
Vol 37 (11) ◽  
pp. 1835-1849
Author(s):  
Yonggang Chen ◽  
Weinan Chen ◽  
Lei Zhu ◽  
Zerong Su ◽  
Xuefeng Zhou ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Sensor Fusion ◽  
Localization Problem ◽  
Global Localization ◽  
Localization Accuracy ◽  
Vision Sensors ◽  
Speed Up ◽  
The Rich ◽  
Improved Performance ◽  
Hybrid Grid

SummaryEstimating the robot state within a known map is an essential problem for mobile robot; it is also referred to “localization”. Even LiDAR-based localization is practical in many applications, it is difficult to achieve global localization with LiDAR only for its low-dimension feedback, especially in environments with repetitive geometric features. A sensor-fusion-based localization system is introduced in this paper, which has the capability of addressing the global localization problem. Both LiDAR and vision sensors are integrated, making use of the rich information introduced by vision sensor and the robustness from LiDAR. A hybrid grid-map is built for global localization, and a visual global descriptor is applied to speed up the localization convergence, combined with a pose refining pipeline for improving the localization accuracy. Also, a trigger mechanism is introduced to solve kidnapped problem and verify the relocalization result. The experiments under different conditions are designed to evaluate the performance of the proposed approach, as well as a comparison with the existing localization systems. According to the experimental results, our system is able to solve the global localization problem, and the sensor-fusion mechanism in our system has an improved performance.

scholarly journals Autonomous navigation of indoor mobile robots using a global ultrasonic system

Robotica ◽  
2004 ◽  
Vol 22 (4) ◽  
pp. 369-374 ◽  
Author(s):  
Soo-Yeong Yi ◽  
Byoung-Wook Choi
Keyword(s):  
Mobile Robot ◽  
Autonomous Navigation ◽  
A Priori ◽  
Dead Reckoning ◽  
The Self ◽  
Localization Algorithm ◽  
Ground Vehicles ◽  
Trajectory Error ◽  
Indoor Mobile Robot ◽  
Embedded Controller

Autonomous navigation of an indoor mobile robot, using the global ultrasonic system, is presented in this paper. Since the trajectory error of the dead-reckoning navigation increases significantly with time and distance, the autonomous navigation system of a mobile robot requires self-localization capa-bility in order to compensate for trajectory error. The global ultrasonic system, consisting of four ultrasonic generators fixed at a priori known positions in the work space and two receivers mounted on the mobile robot, has a similar structure to the well-known satellite GPS(Global Positioning System), which is used for the localization of ground vehicles. The EKF (Extended Kalman Filter) algorithm is utilized for self-localization and autonomous navigation, based on the self-localization algorithm is verified by experiments performed in this study. Since the self-localization algorithm is efficient and fast, it is appropriate for an embedded controller of a mobile robot.

scholarly journals A Robust, Non-Cooperative Localization Algorithm in the Presence of Outlier Measurements in Ocean Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2708 ◽  
Author(s):  
Xiaojun Mei ◽  
Huafeng Wu ◽  
Jiangfeng Xian ◽  
Bowen Chen ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Sensor Networks ◽  
Large Scale ◽  
Trust Region ◽  
Accurate Solution ◽  
Least Square ◽  
Cooperative Localization ◽  
Localization Problem ◽  
Localization Algorithm ◽  
Localization Accuracy ◽  
Ocean Sensor Networks

As an important means of multidimensional observation on the sea, ocean sensor networks (OSNs) could meet the needs of comprehensive information observations in large-scale and multifactor marine environments. In what concerns OSNs, accurate location information is the basis of the data sets. However, because of the multipath effect—signal shadowing by waves and unintentional or malicious attacks—outlier measurements occur frequently and inevitably, which directly degrades the localization accuracy. Therefore, increasing localization accuracy in the presence of outlier measurements is a critical issue that needs to be urgently tackled in OSNs. In this case, this paper proposed a robust, non-cooperative localization algorithm (RNLA) using received signal strength indication (RSSI) in the presence of outlier measurements in OSNs. We firstly formulated the localization problem using a log-normal shadowing model integrated with a first order Taylor series. Nevertheless, the problem was infeasible to solve, especially in the presence of outlier measurements. Hence, we then converted the localization problem into the optimization problem using squared range and weighted least square (WLS), albeit in a nonconvex form. For the sake of an accurate solution, the problem was then transformed into a generalized trust region subproblem (GTRS) combined with robust functions. Although GTRS was still a nonconvex framework, the solution could be acquired by a bisection approach. To ensure global convergence, a block prox-linear (BPL) method was incorporated with the bisection approach. In addition, we conducted the Cramer–Rao low bound (CRLB) to evaluate RNLA. Simulations were carried out over variable parameters. Numerical results showed that RNLA outperformed the other algorithms under outlier measurements, notwithstanding that the time for RNLA computation was a little bit more than others in some conditions.

scholarly journals A novel navigation system for an autonomous mobile robot in an uncertain environment

Robotica ◽  
2021 ◽  
pp. 1-26
Author(s):  
Meng-Yuan Chen ◽  
Yong-Jian Wu ◽  
Hongmei He
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Autonomous Navigation ◽  
Clustering Algorithm ◽  
Modular Design ◽  
Optimal Path ◽  
Surrounding Environment ◽  
Computing Complexity ◽  
The Arts ◽  
Window Approach

Abstract In this paper, we developed a new navigation system, called ATCM, which detects obstacles in a sliding window with an adaptive threshold clustering algorithm, classifies the detected obstacles with a decision tree, heuristically predicts potential collision and finds optimal path with a simplified Morphin algorithm. This system has the merits of optimal free-collision path, small memory size and less computing complexity, compared with the state of the arts in robot navigation. The modular design of 6-steps navigation provides a holistic methodology to implement and verify the performance of a robot’s navigation system. The experiments on simulation and a physical robot for the eight scenarios demonstrate that the robot can effectively and efficiently avoid potential collisions with any static or dynamic obstacles in its surrounding environment. Compared with the particle swarm optimisation, the dynamic window approach and the traditional Morphin algorithm for the autonomous navigation of a mobile robot in a static environment, ATCM achieved the shortest path with higher efficiency.

scholarly journals CWDV-Hop: A Hybrid Localization Algorithm With Distance-Weight DV-Hop and CSO for Wireless Sensor Networks

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 380-399
Author(s):  
Jiaxing Chen ◽  
Wei Zhang ◽  
Zhihua Liu ◽  
Rui Wang ◽  
Shujing Zhang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Hybrid Localization
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