Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry

Chen, Baifan; Zhao, Haowu; Zhu, Ru‐Yi; Hu, Yemin

doi:10.3390/s22134749

Cited by 5 publications

(7 citation statements)

References 33 publications

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“…The comparison of different technologies to localize a mobile robot, with their advantages and disadvantages, is presented in Table 2. Errors due to the design of the sensors themselves [7], [12], [18], [20], [30][31][32], [35], [48], [49] QRCode/ArUco Low Quick and simple application, easy to change course -new marker Susceptible to soiling [7], [8], [35] RFID Low/ Medium…”

Section: The Classification and Selection Of Localization Methodsmentioning

confidence: 99%

“…Global location determination and simple navigation thanks to extensive maps Limited range (mainly indoors) [12], [17], [28,29], [35], [47] Radio waves Medium Good accuracy, resistant to harsh environmental conditions Application of complex data processing algorithms [20][21][22][23][24][25][26], [49] Vision system High Very high accuracy, can be used virtually anywhere, ambient mapping Mainly based on artificial intelligence, so very sophisticated image recognition algorithms [9][10][11][12], [27], [41], [46] Follow Poor dirt resistance, complex information processing [9], [13], [45,46], [48] Other markers [-] Easy to find (self) robot, with known marker location High density of markers needed for decent work [10], [27], [48] 3. A review of the selected localization methods…”

Section: Gnss (Gps) Mediummentioning

confidence: 99%

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A review on positioning techniques of mobile robots

Semborski,

Idzkowski

2024

Robot. syst., appl.

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In this article, we have reviewed the available positioning, localization and navigation techniques for mobile robots. Different localization techniques based on diverse technologies are compared with one another, along with diverse algorithms and techniques for analyzing this information. The article highlights algorithms based on odometry, triangulation, visual analysis, and marker detection. The analysis included global, local, and personal location. One acquires knowledge on which method is suitable for indoor use and which for outdoor use, as well as the appropriate environmental conditions for each. The accuracy of the individual methods was compared with that of integrated systems consisting of several methods. For practical knowledge, it is possible to determine whether a particular method is cost-effective for a particular solution and to compare the expenses involved.

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Section: The Classification and Selection Of Localization Methodsmentioning

confidence: 99%

Section: Gnss (Gps) Mediummentioning

confidence: 99%

A review on positioning techniques of mobile robots

Semborski,

Idzkowski

2024

Robot. syst., appl.

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“…The fusion positioning method combines the advantages of the absolute positioning method, which has high accuracy in perceiving the surroundings, and the relative positioning method, which does not rely on the external environment. [12,20,21]. Therefore, it has been widely used in recent years [22].…”

Section: Introductionmentioning

confidence: 99%

“…The LiDAR has the advantages of high positioning accuracy, large data, and less susceptibility to weather [28]. This makes LiDAR widely used in crop detection and navigation [21,29,30]. Among them, horizontally mounted LiDAR is used for navigation and vertically mounted for crop detection [31,32].…”

Section: Introductionmentioning

confidence: 99%

Precision Inter-Row Relative Positioning Method by Using 3D LiDAR in Planted Forests and Orchards

Liu,

Ji,

Zeng

et al. 2024

Agronomy

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Accurate positioning at the inter-row canopy can provide data support for precision variable-rate spraying. Therefore, there is an urgent need to design a reliable positioning method for the inter-row canopy of closed orchards (planted forests). In the study, the Extended Kalman Filter (EKF) fusion positioning method (method C) was first constructed by calibrating the IMU and encoder with errors. Meanwhile, 3D Light Detection and Ranging (LiDAR) observations were introduced to be fused into Method C. An EKF fusion positioning method (method D) based on 3D LiDAR corrected detection was designed. The method starts or closes method C by the presence or absence of the canopy. The vertically installed 3D LiDAR detected the canopy body center, providing the vehicle with inter-row vertical distance and heading. They were obtained through the distance between the center of the body and fixed row spacing. This can provide an accurate initial position for method C and correct the positioning trajectory. Finally, the positioning and canopy length measurement experiments were designed using a GPS positioning system. The results show that the method proposed in this study can significantly improve the accuracy of length measurement and positioning at the inter-row canopy, which does not significantly change with the distance traveled. In the orchard experiment, the average positioning deviations of the lateral and vertical distances at the inter-row canopy are 0.1 m and 0.2 m, respectively, with an average heading deviation of 6.75°, and the average relative error of canopy length measurement was 4.35%. The method can provide a simple and reliable inter-row positioning method for current remote-controlled and manned agricultural machinery when working in standardized 3D crops. This can modify the above-mentioned machinery to improve its automation level.

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“…Most of them mainly begin with two aspects of an integrated system. One aspect is to improve the system input, such as preprocessing the data, or adding auxiliary sensors to form a multi-source information fusion system and using other auxiliary sensor information to correct the INS [15,16]. The other aspect is to improve the processing methods of INS and GNSS data.…”

Section: Introductionmentioning

confidence: 99%

An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

et al. 2023

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When the satellite signal is lost or interfered with, the traditional GNSS (Global Navigation Satellite System)/INS (Inertial Navigation System) integrated navigation will degenerate into INS, which results in the decrease in navigation accuracy. To solve these problems, this paper mainly established the PSO (particle swarm optimization) -LSTM (Long Short-Term Memory) neural network model to predict the increment of GNSS position under the condition of satellite rejection and accumulation to obtain the pseudo-GNSS signal. The signal is used to compensate for the observed value in the integrated system. The model takes the advantages of LSTM, which is good at processing time series, and uses PSO to obtain the optimal value of important hyperparameters efficiently. Meanwhile, the improved threshold function is used to denoise the IMU (inertial measurement unit) data, which improves the SNR (signal-to-noise ratio) of IMU outputs effectively. Finally, the performance of the algorithm is proved by actual road test. Compared with INS, the method can reduce the maximum errors of latitude and longitude by at least 98.78% and 99.10% while the satellite is lost for 60 s, effectively improving the accuracy of the GNSS/INS system in satellite rejection.

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Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry

Cited by 5 publications

References 33 publications

A review on positioning techniques of mobile robots

A review on positioning techniques of mobile robots

Precision Inter-Row Relative Positioning Method by Using 3D LiDAR in Planted Forests and Orchards

An GNSS/INS Integrated Navigation Algorithm Based on PSO-LSTM in Satellite Rejection

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