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Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8228
Author(s):  
Yunbing Hu ◽  
Ao Peng ◽  
Biyu Tang ◽  
Hongying Xu

The inertial navigation system has high short-term positioning accuracy but features cumulative error. Although no cumulative error occurs in WiFi fingerprint localization, mismatching is common. A popular technique thus involves integrating an inertial navigation system with WiFi fingerprint matching. The particle filter uses dead reckoning as the state transfer equation and the difference between inertial navigation and WiFi fingerprint matching as the observation equation. Floor map information is introduced to detect whether particles cross the wall; if so, the weight is set to zero. For particles that do not cross the wall, considering the distance between current and historical particles, an adaptive particle filter is proposed. The adaptive factor increases the weight of highly trusted particles and reduces the weight of less trusted particles. This paper also proposes a multidimensional Euclidean distance algorithm to reduce WiFi fingerprint mismatching. Experimental results indicate that the proposed algorithm achieves high positioning accuracy.


2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Zou Zhou ◽  
Guoli Zhang ◽  
Fei Zheng ◽  
Tuyang Wang ◽  
Longjie Chen ◽  
...  

Robots can use echo signals for simultaneous localization and mapping (SLAM) services in unknown environments where its own camera is not available. In current acoustic SLAM solutions, the time of arrival (TOA) in the room impulse response (RIR) needs to be associated with the corresponding reflected wall, which leads to an echo labelling problem (ELP). The position of the wall can be derived from the TOA associated with the wall, but most of the current solutions ignore the effect of the cumulative error in the robot’s moving state measurement on the wall position estimation. In addition, the estimated room map contains only the shape information of the room and lacks position information such as the positions of doors and windows. To address the above problems, this paper proposes a graph optimization-based acoustic SLAM edge computing system offering centimeter-level mapping services with reflector recognition capability. In this paper, a robot equipped with a sound source and a four-channel microphone array travels around the room, and it can collect the room impulse response at different positions of the room and extract the RIR cepstrum feature from the room impulse response. The ELP is solved by using the RIR cepstrum to identify reflectors with different absorption coefficients. Then, the similarity of the RIR cepstrum vectors is used for closed-loop detection. Finally, this paper proposes a method to eliminate the cumulative error of robot movement by fusing IMU data and acoustic echo data using graph-optimized edge computation. The experiments show that the acoustic SLAM system in this paper can accurately estimate the trajectory of the robot and the position of doors, windows, and so on in the room map. The average self-localization error of the robot is 2.84 cm, and the mapping error is 4.86 cm, which meet the requirement of centimeter-level map service.


Micromachines ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1227
Author(s):  
Xuesen Zhao ◽  
Rongkai Tan ◽  
Zhe Wang ◽  
Xicong Zou ◽  
Zhenjiang Hu ◽  
...  

In this paper, a novel positioner fixture with a high repeated positioning accuracy and a high stiffness is proposed and investigated. A high-precision end-toothed disc is used to achieve the high repeated positioning accuracy of the designed positioner fixture. The mathematical models of the cumulative error of the tooth pitch, the tooth alignment error and the error of the tooth profile half-angle of the end-toothed disc are analyzed. The allowable tolerance values of the cumulative error of the tooth pitch, the tooth alignment error and the error of the tooth profile half-angle of the end-toothed disc are given. According to the theoretical calculation results, a prototype positioner fixture is fabricated and its repeated positioning accuracy and stiffness are tested. The test results indicate that the stiffness of the proposed positioner fixture is 1050.5 N/μm, which is larger than the previous positioner fixtures of the same type. The repeated positioning accuracy of the proposed positioner fixture in the x, y and z directions are ± 0.48 μm, ± 0.45 μm and ± 0.49 μm, respectively, which is significantly higher than the previous positioner fixtures.


2021 ◽  
Vol 9 ◽  
Author(s):  
Haifeng Luo ◽  
Xun Dou ◽  
Rong Sun ◽  
Shengjun Wu

Wind power generation is likely to hinder the safe and stable operations of power systems for its irregularity, intermittency, and non-smoothness. Since wind power is continuously connected to power systems, the step length required for predicting wind power is increasingly extended, thereby causing an increasing cumulative error. Correcting the cumulative error to predict wind power in multi-step is an urgent problem that needs to be solved. In this study, a multi-step wind power prediction method was proposed by exploiting improved TCN to correct the cumulative error. First, multi-scale convolution (MSC) and self-attentiveness (SA) were adopted to optimize the problem that a single-scale convolution kernel of TCN is difficult to extract temporal and spatial features at different scales of the input sequence. The MSC-SA-TCN model was built to recognize and extract different features exhibited by the input sequence to improve the accuracy and stability of the single-step prediction of wind power. On that basis, the multi-channel time convolutional network with multiple input and multiple output codec technologies was adopted to build the nonlinear mapping between the output and input of the TCN multi-step prediction. The method improved the problem that a single TCN is difficult to tap the different nonlinear relationships between the multi-step prediction output and the fixed input. The MMED-TCN multi-step wind power prediction model was developed to separate linearity and nonlinearity between input and output to reduce the multi-step prediction error. An experimental comparative analysis was conducted based on the measured data from two wind farms in Shuangzitai, Liaoning, and Keqi, Inner Mongolia. As revealed from the results, the MAE and RMSE of the MMED-TCN-based multi-step prediction model achieved the cumulative mean values of 0.0737 and 0.1018. The MAE and RMSE metrics outperformed those of the VMD-AMS-TCN and MSC-SA-TCN models. It can be seen that the wind power prediction method proposed in this study could improve the feature extraction ability of TCN for input sequences and the ability of mining the mapping relationship between multiple inputs and multiple outputs. The method is superior in terms of the accuracy and stability of wind power prediction.


Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5522
Author(s):  
Gang Peng ◽  
Zezao Lu ◽  
Jiaxi Peng ◽  
Dingxin He ◽  
Xinde Li ◽  
...  

Currently, simultaneous localization and mapping (SLAM) is one of the main research topics in the robotics field. Visual-inertia SLAM, which consists of a camera and an inertial measurement unit (IMU), can significantly improve robustness and enable scale weak-visibility, whereas monocular visual SLAM is scale-invisible. For ground mobile robots, the introduction of a wheel speed sensor can solve the scale weak-visibility problem and improve robustness under abnormal conditions. In this paper, a multi-sensor fusion SLAM algorithm using monocular vision, inertia, and wheel speed measurements is proposed. The sensor measurements are combined in a tightly coupled manner, and a nonlinear optimization method is used to maximize the posterior probability to solve the optimal state estimation. Loop detection and back-end optimization are added to help reduce or even eliminate the cumulative error of the estimated poses, thus ensuring global consistency of the trajectory and map. The outstanding contribution of this paper is that the wheel odometer pre-integration algorithm, which combines the chassis speed and IMU angular speed, can avoid the repeated integration caused by linearization point changes during iterative optimization; state initialization based on the wheel odometer and IMU enables a quick and reliable calculation of the initial state values required by the state estimator in both stationary and moving states. Comparative experiments were conducted in room-scale scenes, building scale scenes, and visual loss scenarios. The results showed that the proposed algorithm is highly accurate—2.2 m of cumulative error after moving 812 m (0.28%, loopback optimization disabled)—robust, and has an effective localization capability even in the event of sensor loss, including visual loss. The accuracy and robustness of the proposed method are superior to those of monocular visual inertia SLAM and traditional wheel odometers.


Electronics ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1298
Author(s):  
Riccardo Carotenuto ◽  
Fortunato Pezzimenti ◽  
Francesco G. Della Corte ◽  
Demetrio Iero ◽  
Massimo Merenda

The recent growing interest in indoor positioning applications has paved the way for the development of new and more accurate positioning techniques. The envisioned applications, include people and asset tracking, indoor navigation, as well as other emerging market applications, require fast and precise positioning. To this end, the effectiveness and high accuracy and refresh rate of positioning systems based on ultrasonic signals have been already demonstrated. Typically, positioning is obtained by combining multiple ranging. In this work, it is shown that the performance of a given ultrasonic airborne ranging technique can be thoroughly analyzed using renowned academic acoustic simulation software, originally conceived for the simulation of echographic transducers and systems. Here, in order to show that the acoustic simulation software can be profitably applied to ranging systems in air, an example is provided. Simulations are performed for a typical ultrasonic chirp, from an ultrasound emitter, in a typical office room. The ranging performances are evaluated, including the effects of acoustic diffraction and air frequency dependent absorption, when the signal-to-noise ratio (SNR) decreases from 30 to −20 dB. The ranging error, computed over a point grid in the space, and the ranging cumulative error distribution is shown for different SNR levels. The proposed approach allowed us to estimate a ranging error of about 0.34 mm when the SNR is greater than 0 dB. For SNR levels down to −10 dB, the cumulative error distribution shows an error below 5 mm, while for lower SNR, the error can be unlimited.


Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1196
Author(s):  
Gang Li ◽  
Yawen Zeng ◽  
Huilan Huang ◽  
Shaojian Song ◽  
Bin Liu ◽  
...  

The traditional simultaneous localization and mapping (SLAM) system uses static points of the environment as features for real-time localization and mapping. When there are few available point features, the system is difficult to implement. A feasible solution is to introduce line features. In complex scenarios containing rich line segments, the description of line segments is not strongly differentiated, which can lead to incorrect association of line segment data, thus introducing errors into the system and aggravating the cumulative error of the system. To address this problem, a point-line stereo visual SLAM system incorporating semantic invariants is proposed in this paper. This system improves the accuracy of line feature matching by fusing line features with image semantic invariant information. When defining the error function, the semantic invariant is fused with the reprojection error function, and the semantic constraint is applied to reduce the cumulative error of the poses in the long-term tracking process. Experiments on the Office sequence of the TartanAir dataset and the KITTI dataset show that this system improves the matching accuracy of line features and suppresses the cumulative error of the SLAM system to some extent, and the mean relative pose error (RPE) is 1.38 and 0.0593 m, respectively.


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