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Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Aiming at the specific operation path cruising problem of greenhouse tomato picking robots, this paper designs a laser SLAM (Simultaneous Localization And Mapping)-based autonomous navigation system for greenhouse tomato picking robots.The autonomous navigation system utilizes LiDAR to detect the greenhouse environment, creates an environment map with the Gmapping algorithm employing particle filtering, locates the robot with the AMCL (Adaptive Monte Carlo Localization) algorithm, devises the robot's path for picking tasks with the A* algorithm, and dynamically navigates around obstacles during operations with the DWA (Dynamic Window Approach) algorithm. The greenhouse experiment revealed that the robot's average deviance for both directions (X and Y) is 2.87 and 0.80 centimeters, respectively, when traveling a 320 centimeter path. On the other hand, its average variation across the directions of X and Y is 1.48 versus 1.08 centimeters when it passes through a 40 centimeter zone. The robot exhibits averaged variations of 1.73 and 2.24 millimeters in the X and Y axes, each, when navigating a 40-centimeter-radius turn. Furthermore, the robot exhibits average variations of 1.12 centimeters in the X and 0.78 centimeters in the Y directions when it follows a certain "S"-shaped course. Based on the fulfillment of prescribed path cruising, the system has a high level of navigation and positioning accuracy, better meeting the requirements for greenhouse tomato harvesting robot direction and placement. [ABSTRACT FROM AUTHOR]

In many real navigation problems, moving objects may have some state or measurement constraints along their way. Using these constraints in conventional Extended Kalman Filter (EKF) equations results large matrices which are computationally time-consuming. In this paper, a new Constrained Navigation Filter (CNF) is proposed as a parallel to reduce the computational burden of the conventional EKF algorithm while increasing the positioning accuracy. So a methodology has been developed for Strap-down Inertial Navigation System (SINS) based on MEMS IMU applied on Pipeline Inspection Gauge (PIG) to sense data at constant sampling rate of 108 km of the pipeline. The results verified that using such a hybrid approach has improved positional accuracy 8.97% in comparison with that of the latest methods like EKF/ Pipe Line Junctions (PLJ). Also, the proposed method is 2.277 times better than EKF/PLJ in the algorithm runtime. [ABSTRACT FROM AUTHOR]

Aiming at the specific operation path cruising problem of greenhouse tomato picking robots, this paper designs a laser SLAM (Simultaneous Localization And Mapping)-based autonomous navigation system for greenhouse tomato picking robots.The autonomous navigation system utilizes LiDAR to detect the greenhouse environment, creates an environment map with the Gmapping algorithm employing particle filtering, locates the robot with the AMCL (Adaptive Monte Carlo Localization) algorithm, devises the robot's path for picking tasks with the A* algorithm, and dynamically navigates around obstacles during operations with the DWA (Dynamic Window Approach) algorithm. The greenhouse experiment revealed that the robot's average deviance for both directions (X and Y) is 2.87 and 0.80 centimeters, respectively, when traveling a 320 centimeter path. On the other hand, its average variation across the directions of X and Y is 1.48 versus 1.08 centimeters when it passes through a 40 centimeter zone. The robot exhibits averaged variations of 1.73 and 2.24 millimeters in the X and Y axes, each, when navigating a 40-centimeter-radius turn. Furthermore, the robot exhibits average variations of 1.12 centimeters in the X and 0.78 centimeters in the Y directions when it follows a certain ''S''-shaped course. Based on the fulfillment of prescribed path cruising, the system has a high level of navigation and positioning accuracy, better meeting the requirements for greenhouse tomato harvesting robot direction and placement.

Positioning and mapping technology is a difficult and hot topic in autonomous driving environment sensing systems. In a complex traffic environment, the signal of the Global Navigation Satellite System (GNSS) will be blocked, leading to inaccurate vehicle positioning. To ensure the security of automatic electric campus vehicles, this study is based on the Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain (LEGO-LOAM) algorithm with a monocular vision system added. An algorithm framework based on Lidar-IMU-Camera (Lidar means light detection and ranging) fusion was proposed. A lightweight monocular vision odometer model was used, and the LEGO-LOAM system was employed to initialize monocular vision. The visual odometer information was taken as the initial value of the laser odometer. At the back-end opti9mization phase error state, the Kalman filtering fusion algorithm was employed to fuse the visual odometer and LEGO-LOAM system for positioning. The visual word bag model was applied to perform loopback detection. Taking the test results into account, the laser radar loopback detection was further optimized, reducing the accumulated positioning error. The real car experiment results showed that our algorithm could improve the mapping quality and positioning accuracy in the campus environment. The Lidar-IMU-Camera algorithm framework was verified on the Hong Kong city dataset UrbanNav. Compared with the LEGO-LOAM algorithm, the results show that the proposed algorithm can effectively reduce map drift, improve map resolution, and output more accurate driving trajectory information.

Objective In urban rail transit systems, train velocity measuring and positioning technology offers critical information to many applications such as train autonomous driving, collision avoidance, dispatching and directing. The architecture and function of the train control systems are increasingly complex, and the continuous level-up of the operation efficiency and the services requires higher performance for train velocity measuring and positioning, so that the ability of the ATP (automatic train protection) measuring velocity and positioning in different scenarios needs to be improved. Method A train velocity measurement approach based on wheel-axle creep rate detection is proposed. First, the architecture of the train velocity measurement and localization system is introduced and the technical process of train velocity measurement based on wheel-axle creep rate is explained. Then the wheel-axle creep theory is explained and the velocity measurement error of the Doppler radar sensor is modeled to obtain the radar maximum reference velocity, which is used to calculate the wheel-axle creep. Different compensation for the odometer velocity is applied based on different wheel-axle creep status. Finally, the velocity measurement approach proposed is verified using the experimental data sets. Result & Conclusion The experimental results show that most of the time the wheel-axle is in a low creep state during the train braking stage. The velocity obtained by less compensation for the odometer velocity is much lower than the maximum velocity calculated by the existing ATP. This approach remarkably improves the velocity measurement accuracy under the premise of safety when the train minimal velocity is identical.

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The centimetre-level accuracy of Ultra-wideband (UWB) has attracted significant attention in indoor positioning. However, the precision of UWB positioning is severely compromised by non-line-of-sight (NLOS) conditions that arise from complex indoor environments. On the other hand, odometry is widely applicable to wheeled robots due to its reliable short-term accuracy and high sampling frequency, but it suffers from long-term drift. This paper proposes a tightly coupled fusion method with a Dynamic Unscented Kalman Filter (DUKF), which utilises odometry to identify and mitigate NLOS effects on UWB measurements. Horizontal Dilution of Precision (HDOP) was introduced to assess the impact of geometric distribution between robots and UWB anchors on UWB positioning accuracy. By dynamically adjusting UKF parameters based on NLOS condition, HDOP values, and robot motion status, the proposed method achieves excellent UWB positioning results in a severe NLOS environment, which enables UWB positioning even when only one line-of-sight (LOS) UWB anchor is available. Experimental results under severe NLOS conditions demonstrate that the proposed system achieves a Root Mean Square Error (RMSE) of approximately 7.5 cm. [ABSTRACT FROM AUTHOR]

Copyright of Urban Mass Transit is the property of Urban Mass Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To meet the requirements for automated testing during equipment maintenance for odometers in urban rail transit, this paper first compares different measurement methods of odometers, and then proposes a quick measurement method of odometers that is suitable for field maintenance. It also makes the proposal that, to improve the accuracy of the test results of odometers, statistical analysis of their dynamic performance indicators should be performed, rather than testing and analysis of their static performance indicators. The proposed quick measurement method of odometers has been verified through prototype testing and put into practical application, and has produced good application results. [ABSTRACT FROM AUTHOR]

The technology for determining the location of an object on the ground is the foundation for building vehicle navigation systems and surveillance systems after them. In general, such navigation systems make it possible to determine the coordinates of the vehicle's location and its directional angle, find the direction to the destination, and calculate the coordinates of observation objects. The technical implementation of systems can be quite diverse - from implementation on mechanical elements to their implementation using microelectronics elements. The main factors in the development of navigation systems are the transition to computer-integrated systems and resource-saving technologies. This will make it possible to increase the accuracy of navigation information, obtain a significant gain in the weight and size characteristics of the equipment, reduce its energy consumption and increase reliability. The possibility of modernization of odometrical navigation systems based on integrated computer technologies is considered. [ABSTRACT FROM AUTHOR]

Continuous information on the current location of a vehicle in its infrastructure is essential for automated driving. However, industrial railways and shunting operations may entail boundary conditions which are not suitable for widespread satellite-based positioning. Hence, this paper presents a positioning system, based on radio-frequency identification (RFID) and odometer, which was developed and tested on an automated road-rail vehicle for shunting at low velocities. The achieved accuracy for discrete positioning with RFID was estimated at 0.22 m. In quasi-continuous positioning between consecutive RFID tags, the odometer exhibited an error of −2.2% to 0.7% of travelled distance depending on the investigated stationary driving states and sensor data evaluation. Apart from enhancement potentials for the RFID system, the results also indicated the odometer to be prone to the shunting vehicle dynamics when the rotation of its rail wheels is measured. Furthermore, the shunting vehicle appears to run constantly with a notable lateral displacement even on straight tracks due to the property of its running gear, causing a systematic underestimation of the travelled distance. [ABSTRACT FROM AUTHOR]

At present, the track type robot is widely used in the inspection robot of the fully mechanized working face. When the robot passes through the track connection, it will produce jitter. It causes an increase in the positioning error of the inertial navigation/odometer combination. In order to solve this problem, based on the integrated navigation algorithm of inertial navigation/odometer, a piecewise filtering method based on jitter detection is adopted to achieve autonomous positioning of the inspection robot. Based on the gyroscope data of the inspection robot passing through the track connector, a sliding window method is used to dynamically analyze the pitch angular velocity of the robot. The local maximum rising edge and local maximum falling edge are determined by calculating the derivative sum. When the maximum rising edge and maximum falling edge alternately appear, it is considered that the track connector has been recognized. The jitter detection is achieved, thus dividing the robot's motion state into stable operation state and jitter state. When the robot is in a stable operation state, both the gyroscope and odometer data are relatively stable. At this time, the inertial navigation/odometer combination navigation method is used for filtering and solving. The gyroscope error is corrected based on the characteristic that the gyroscope data should be stable near zero. When the robot is in a jitter state, the odometer may generate errors due to wheel slip and bouncing in the air. At this time, a pure inertial navigation algorithm is used to eliminate the impact of odometer errors on the integrated navigation positioning. The experimental results show that the jitter detection algorithm can accurately determine the track connections. The segmented filtering method based on jitter detection can effectively improve the positioning precision of the inspection robot. The average positioning error is less than 5 mm, meeting the precise positioning requirements of the fully mechanized working face.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper deals with odometers (i.e. adding machines) of general type. We give a characterization of self-conjugacies of odometers which enables us to present an elementary proof of a classification of odometers given by Buescu and Stewart in [2]. The paper might also serve as a very quick introduction to odometers.

At present, the shearer positioning method is based on the combination of the inertial navigation system and odometer. The method directly uses the output of the odometer to correct the shearer forward speed calculated by the inertial navigation system. However, the capability of suppressing the error divergence of the inertial navigation system is very limited. The shearer in the process of movement meets the characteristics of the non-holonomic constraints. When the shearer does not jump and sideslip, the lateral velocity and vertical velocity at the connection between the traction gear and the crawler are zero. Based on this characteristic, a new shearer positioning method based on non-holonomic constraints is proposed on the basis of the combination of the inertial navigation system and odometer. The output of the inertial measurement unit arranged in the middle of the shearer's body is mechanically arranged, so as to obtain the attitude, speed and position information of the shearer. The output of the odometer installed on the traction gear of the shearer is used to calculate the instantaneous velocity of the shearer. The Kalman filtering state equation is established by using a mechanical arrangement result of the inertial navigation system and an error propagation model. The non-integrity constraint is introduced at the joint of a traction gear and a crawler of the shearer. The Kalman filtering observation equation is established by using the difference between the velocity projected at the joint by the inertial navigation system and the velocity output by the mileometer as an observation vector. The output of the inertial navigation system is modified by using the results of the Kalman filtering algorithm as error feedback. Then the optimal estimation of the attitude, speed and position of the shearer is obtained. The experimental results show that compared with the traditional combined positioning method of inertial navigation system and odometer, the positioning error does not diverge with time after the non-holonomic constraint is added. The positioning method has good tracking performance on the actual trajectory. The positioning errors of the shearer in the forward, lateral and vertical directions are reduced by 66%, 62% and 67% respectively.

Since 23 June 2020, BDS-3 has been entirely operated and obtained the ability of global PNT (Positioning, Navigation, and Timing) services. Afterward, real-time Precise Point Positioning (PPP) service is available in China's surrounding areas via BDS-3 PPP-B2b signal. However, such a real-time PPP service cannot maintain the high accuracy and continuity of positioning solutions in challenging environments, such as urban environments. For that, we carried out a model by integrating between-satellite single-differenced (BSSD) PPP, a low-cost Inertial Navigation System (INS), and an odometer via an extended Kalman filter. The performance of this integration model was assessed with vehicle-borne data. Results demonstrated that (1) the position RMS (Root Mean Square) of BSSD PPP are 64.33 cm, 53.47 cm, and 154.11 cm. Compared with BSSD PPP, about 31.2%, 23.3%, and 27.3% position improvements can be achieved by using INS. Further enhancements of position RMS benefiting from the odometer are 1.34%, 1.41%, and 1.73% in the three directions. (2) Anyway, the accuracy of BSSD PPP/INS/Odometer tightly coupled integration is slightly higher than that of undifferenced PPP/INS/Odometer integration, with average improvement percentages of 7.71%, 3.09%, and 0.27%. Meanwhile, the performance of BSSD PPP/INS/Odometer integration during the periods with satellite outages is better than the undifferenced PPP-based solutions. (3) The improvements in attitudes from an odometer are more significant on heading angle than the other two attitudes, with percentages of 25.00%. (4) During frequent GNSS outage periods, the reduction in average maximum position drifts provided by INS are 18.01%, 8.95%, and 20.74%. After integrating with an odometer, the drifts can be furtherly decreased by 25.11%, 15.96%, and 20.69%. For attitude, about 41.67% reduction in average maximum drifts of heading angles is obtained. [ABSTRACT FROM AUTHOR]

Real-time positioning in suburban and urban environments has been a challenging task for many Intelligent Transportation Systems (ITS) applications. In these environments, positioning using Global Navigation Satellite Systems (GNSS) cannot provide continuous solutions due to the blockage of signals in harsh scenarios. Consequently, it is intrinsic to have an independent positioning system capable of providing accurate and reliable positional solutions over GNSS outages. This study exploits the integration of Light Detection and Ranging (LiDAR), gyroscope, and odometer sensors, and a novel real-time algorithm is proposed for this integration. Real field data, collected by a moving land vehicle, is used to test the presented algorithm. Three simulated GNSS outages are introduced in the trajectory such that each outage lasts for five minutes. The results show that using the proposed algorithm can achieve a promising navigation performance in urban environments. In addition, it is shown that the denser environments, that existed over the second and third outages, can provide better positioning accuracies as more features are extracted. The horizontal errors over the first outage, with less density of surroundings, reached 7.74 m (0.43%) error with a mean value of 3.15 m. Moreover, the horizontal errors in the denser environments over the second and third outages reached 4.97 m (0.28%) and 3.99 m (0.23%), with mean values of 2.25 m and 1.89 m, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Ordnance Industry Automation is the property of Editorial Board for Ordnance Industry Automation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Using techniques developed by Kanigowski, Lemańczyk, and Radziwiłł [Fund. Math. 255 (2021), pp. 309–336], we verify Sarnak's conjecture for two classes of rank-one subshifts with unbounded cutting parameters. The first class of rank-one subshifts we consider is called almost complete congruency classes (accc), the definition of which is motivated by the main result of Foreman, Gao, Hill, Silva, and Weiss [Isr. J. Math., To appear], which implies that when a rank-one subshift carries a unique nonatomic invariant probability measure, it is accc if it is measure-theoretically isomorphic to an odometer. The second class we consider consists of Katok's map and its generalizations. [ABSTRACT FROM AUTHOR]

In the traditional shearer-positioning method, an odometer is used to assist the forward velocity correction of the inertial navigation system, but it cannot restrain the system's error divergence. For this reason, this paper proposes a shearer positioning method based on non-holonomic constraints. In this method, an inertial measurement unit and odometer are installed in the middle of the shearer body and on the traction gear, respectively, the shearer attitude, speed, and position information are calculated through the inertial measurement-unit mechanization, and the shearer's instantaneous velocity is calculated through the output of the odometer. The mechanization and error transfer process of the inertial navigation system are used to establish a Kalman filtering-state equation. The Kalman filtering observation equation is established through the difference between the projected velocity of the inertial navigation system at the joint and the output velocity of the odometer as the observation vector, and the non-holonomic constraint is introduced. Finally, the error feedback is derived from the results processed by the Kalman filtering algorithm, and the output of the inertial navigation system is corrected to obtain the optimal estimation of the shearer's attitude, speed and position. The experiment shows that compared with the traditional inertial navigation and odometer combined positioning method, the degree of divergence in the positioning results over time is significantly reduced after adding the non-holonomic constraint. The positioning method has good tracking ability for the trajectory of the shearer. The error of the positioning results in the forward direction is reduced by 66%, the lateral direction is reduced by 62%, and the vertical direction is reduced by 67%. [ABSTRACT FROM AUTHOR]

To improve the motion distortion caused by LiDAR data at low and medium frame rates when moving, this paper proposes an improved algorithm for scanning matching of estimated velocity that combines an IMU and odometer. First, the information of the IMU and the odometer is fused, and the pose of the LiDAR is obtained using the linear interpolation method. The ICP method is used to scan and match the LiDAR data. The data fused by the IMU and the odometer provide the optimal initial value for the ICP. The estimated speed of the LiDAR is introduced as the termination condition of the ICP method iteration to realize the compensation of the LiDAR data. The experimental comparative analysis shows that the algorithm is better than the ICP algorithm and the VICP algorithm in matching accuracy. [ABSTRACT FROM AUTHOR]

A case study is presented here on the continued development efforts at the Bhabha Atomic Research Centre on instrumented pipeline inspection gauge for Indian Oil Corporation Limited over the last two decades. A marvel of technology involving challenges of nondestructive testing techniques, mechanical, magnetism, electronics, data processing and analysis, operational safety requirements and portability, qualifications of the tools and characterization of defects with acceptable accuracy were achieved to be at par with the internationally available tools. These tools are being extensively deployed in the field. The homegrown technology for in-line inspection has brought down the cost of the tool and that of the services per kilometre. Different technologies are now being pursued to ensure that the inline inspection work in the country is carried out in an 'Atmanirbhar' way. [ABSTRACT FROM AUTHOR]

The study of the collapse of soils under the effect of flooding is a major problem in soil mechanics. Most of the work done on the treatment of these soils has been devoted to the use of binders of hydraulic or organic types. However, little work has been devoted to the use of salt calcium chloride in collapsible soil treatments. The purpose of this study is to evaluate the effect salt calcium chloride on a reconstituted collapsible soil in the laboratory, at different levels of water content, compaction energy and concentration of the saline solution. The results obtained showed a significant reduction in the potential for soil deformation and an illustration and a noticeable interaction between the soil particles and the saline solution resulting in a denser material. [ABSTRACT FROM AUTHOR]

The integrated navigation system of strapdown inertial navigation system (SINS) and odometer (OD) is a commonly used vehicle positioning system. The installation errors caused by the difference between the installation positions of SINS and OD mainly include the installation deflection angle and lever arm, which will reduce the positioning accuracy. The traditional calibration method is to expand the installation deflection angle error and lever arm error into state variables, the velocity difference under nonholonomic constraint (NHC) is taken as the measurement to establish the Kalman filter model for online calibration, but this method seriously depends on the sensor accuracy. In order to calibrate the installation errors of low-precision SINS, this paper improves NHC, adds the difference between the centripetal acceleration measured by odometer and calculated by SINS as measurement on the basis of the original measurement, and introduces a low-pass filter to reduce the interference of vehicle vibration on the accelerometer.The experimental results show that the proposed algorithm has better convergence of the installation deflection angle, and the error between the estimation results of the forward lever arm and the actual measurement results is less than 0.1m; After compensating the installation errors of the improved NHC, the maximum positioning errors in the east and north directions are reduced by 63.19% and 43.51% respectively, the height error is also greatly reduced, and the positioning accuracy of the integrated navigation system is significantly improved. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Social bees have evolved sophisticated communication systems to recruit nestmates to newly found food sources. As foraging ranges can vary from a few hundred meters to several kilometers depending on the environment or season, populations of social bee species living in different climate zones likely show specific adaptations in their recruitment communication. Accordingly, studies in the western honey bee, Apis mellifera, demonstrated that temperate populations exhibit shallower dance-calibration curves compared with tropical populations. Here, we report the first comparison of calibration curves for three Indian Apis cerana lineages: the tropical Apis indica, and the two montane Himalayan populations Apis cerana cerana (Himachal Pradesh) and Apis cerana kashmirensis (Jammu and Kashmir). We found that the colonies of the two montane A. cerana populations show dance-distance calibration curves with significantly shallower slopes than those of the tropical A. indica. Next, we transferred A. c. cerana colonies to Bangalore (∼ 2600 km away) to obtain calibration curves in the same location as A. indica. The common garden experiment confirmed this difference in slopes, implying that the lineages exhibit genetically fixed differences in dance-distance coding. However, the slopes of the calibration curves of the transferred A. c. cerana colonies were also significantly higher than those of the colonies tested in their original habitat, indicating an important effect of the environment. The differences in dance-distance coding between temperate and tropical A. cerana lineages resemble those described for Apis mellifera, suggesting that populations of both species independently evolved similar adaptations., Competing Interests: Competing interests The authors declare no competing or financial interests., (© 2024. Published by The Company of Biologists Ltd.)

The incorporation of high precision vehicle positioning systems has been demanded by the autonomous electric vehicle (AEV) industry. For this reason, research on visual odometry (VO) and Artificial Intelligence (AI) to reduce positioning errors automatically has become essential in this field. In this work, a new method to reduce the error in the absolute location of AEV using fuzzy logic (FL) is presented. The cooperative data fusion of GPS, odometer, and stereo camera signals is then performed to improve the estimation of AEV localization. Although the most important challenge of this work focuses on the reduction in the odometry error in the vehicle, the defiance of synchrony and the information fusion of sources of different nature is solved. This research is integrated by three phases: data acquisition, data fusion, and statistical evaluation. The first one is data acquisition by using an odometer, a GPS, and a ZED camera in AVE's trajectories. The second one is the data analysis and fuzzy fusion design using the MatLab 2019® fuzzy logic toolbox. The last is the statistical evaluation of the positioning error of the different sensors. According to the obtained results, the proposed model with the lowest error is that which uses all sensors as input (stereo camera, odometer, and GPS). It can be highlighted that the best proposed model manages to reduce the positioning mean absolute error (MAE) up to 25% with respect to the state of the art. [ABSTRACT FROM AUTHOR]

This article deals with error models for a trident quaternion framework proposed in the companion paper (Part I) and further uses them to investigate the odometer-aided static/in-motion inertial navigation attitude alignment for land vehicles. By linearizing the trident quaternion kinematic equation, the left- and right-trident-quaternion error models are obtained, which are found to be equivalent to those derived from profound group affine. The two error models are used to design their corresponding extended Kalman filters (EKFs), namely, the left-quaternion EKF (LQEKF) and the right-quaternion EKF (RQEKF). Simulations and field tests are conducted to evaluate their actual performances. Owing to the high estimation consistency, the LQEKF and the RQEKF converge much faster in the static alignment than the traditional error-model-based EKF, even under arbitrary large heading initialization. For the in-motion alignment, the LQEKF and the RQEKF possess much larger convergence region than the traditional EKF does, although they still require the aid of attitude initialization so as to avoid large initial attitude errors. [ABSTRACT FROM AUTHOR]

In order to meet the requirements of autonomous, accuracy and reliability of vehicle navigation system for weapon launch vehicles, a fault-tolerant integrated navigation scheme of RSINS/odometer is designed. Based on the analysis of the characteristics of different odometer measurement information, the RSINS/odometer integrated navigation system model based on mileage increment matching mode is established. According to the results of the observability analysis of the RSINS/odometer integrated navigation system model, an observable RSINS/odometer integrated navigation system model is established. A fault-tolerant integrated navigation information fusion scheme of RSINS/odometer based on sequential Kalman filter is designed to solve the problem that odometer measurement information may not be available during vehicle driving. Finally, the simulation results show that the RSINS/odometer fault-tolerant integrated navigation scheme can ensure the navigation accuracy and reliability of the integrated system.

For a $ \mathbb{Z}^{d} $-topological dynamical system $ (X, T, \mathbb{Z}^d) $, an isomomorphism is a self-homeomorphism $ \phi : X\to X $ such that for some matrix $ M\in GL(d, \mathbb{Z}) $ and any $ {{\mathit{\boldsymbol{n}}}}\in \mathbb{Z}^{d} $, $ \phi\circ T^{{{\mathit{\boldsymbol{n}}}}} = T^{M{{\mathit{\boldsymbol{n}}}}}\circ \phi $, where $ T^{{\mathit{\boldsymbol{n}}}} $ denote the self-homeomorphism of $ X $ given by the action of $ {{\mathit{\boldsymbol{n}}}}\in {\mathbb Z}^d $. The collection of all the isomorphisms forms a group that is the normalizer of the set of transformations $ T^{{\mathit{\boldsymbol{n}}}} $. In the one-dimensional case, isomorphisms correspond to the notion of flip conjugacy of dynamical systems and by this fact are also called reversing symmetries.Isomorphisms are not well understood even for classical systems. We present a description of them for odometers and more precisely for constant-base $ \mathbb{Z}^{2} $-odometers, which is surprisingly not simple. We deduce a complete description of the isomorphisms of some minimal $ \mathbb{Z}^{d} $-substitutive subshifts. This enables us to provide the first known example of a minimal zero-entropy subshift with the largest possible normalizer group. [ABSTRACT FROM AUTHOR]

In the light of recent developments of the $ \mathcal{S} $-adic study of subshifts, we revisit, within this framework, a well-known result on Toeplitz subshifts due to Jacobs–Keane giving a sufficient combinatorial condition to ensure discrete spectrum. We show that the notion of coincidences, originally introduced in the '$ 70 $s for the study of the discrete spectrum of substitution subshifts, together with the $ \mathcal{S} $-adic structure of the subshift allow to go deeper in the study of Toeplitz subshifts. We characterize spectral properties of the factor maps onto the maximal equicontinuous topological factors by means of coincidences density. We also provide an easy to check necessary and sufficient condition to ensure unique ergodicity for constant length $ \mathcal{S} $-adic subshifts. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To produce a 3D map of the Tehran's first gas transfer pipeline (Tehran—Kuhnamak), a methodology has been developed in this research, in which a strapdown inertial navigation system (SINS) based on micro-electro-mechanical system (MEMS) and inertial measurement unit (IMU) is applied on pipeline inspection gauges (PIGs) to sense data every 4 millimeters of 111 kilometers of the whole pipeline. The navigation solution is based on an extended Kalman filter (EKF) using Allan variance (AVAR) to analyze and tune the EKF initial inputs. A new constrained PIG filter (CPF) is proposed in this paper in integration with EKF, in which two Euler angles (pitch and yaw) of the PIG are updated due to non-holonomic state constraints between pipe junctions. Besides, 98 magnetic control points have been used to increase robustness about every kilometer, which is coordinated by GPS. Furthermore, odometer measurements have been employed as measurements in the EKF. The results show that using such a hybrid approach has improved the PIG positioning accuracy by about 81% compared with that of the Basic EKF. In addition, positioning accuracy in comparison with the latest methods like EKF/pipeline junctions (PLJ) has increased by 32%. Furthermore, the proposed method is 55% better than EKF/PLJ in the algorithm runtime. [ABSTRACT FROM AUTHOR]

In order to improve the positioning accuracy of the geographic coordinate where the center line of long-distance oil and gas pipeline is located, the working principle of the pipeline geographic coordinate measurement system was analyzed, and the error estimation algorithm based on Kalman filter was proposed to compensate the velocity error and odometer error of the navigation system.To lower the error caused by the odometer output signal noise, piecewise linear interpolation method was adopted for data preprocessing to accomplish piecewise trajectory calculation and correction of observation data from inertial navigation system.The simulation test was carried out via a simulation detector equipped with a low-precision inertial navigation system.It is indicated that the trajectory resolved by the strapdown inertial navigation system is consistent with the overall shape and trend of the actual route on test site, and the resolution error is within 1 m.The results show that the error compensation and data preprocessing method based on Kalman filter can effectively filter the step error of mileage under the condition of low-precision measuring devices, and therefore the method is provided with good calculation accuracy and certain application value.

We apply a construction developed in a previous paper by the authors in order to obtain a formula which enables us to compute ℓ 2 -Betti numbers coming from a family of group algebras representable as crossed product algebras. As an application, we obtain a whole family of irrational ℓ 2 -Betti numbers arising from the lamplighter group algebra Q [ Z 2 ≀ Z ] . This procedure is constructive, in the sense that one has an explicit description of the elements realizing such irrational numbers. This extends the work made by Grabowski, who first computed irrational ℓ 2 -Betti numbers from the algebras Q [ Z n ≀ Z ] , where n ≥ 2 is a natural number. We also apply the techniques developed to the generalized odometer algebra O (n ¯) , where n ¯ is a supernatural number. We compute its ∗ -regular closure, and this allows us to fully characterize the set of O (n ¯) -Betti numbers. [ABSTRACT FROM AUTHOR]

For any continuous self-map of a compact metric space, we define, prove the existence, and give an explicit expression of a maximal chain continuous factor. For the purpose, we exploit a chain proximal relation and its extension. An example is given to illustrate a difference of the two relations. An alternative proof of a result on the odometers and the regular recurrence is given. Also, we provide an example of a calculation of the maximal chain continuous factor for generic homeomorphism of the Cantor set. [ABSTRACT FROM AUTHOR]

In Cipriani et al. (2017), the authors proved that, with the appropriate rescaling, the odometer of the (nearest neighbours) divisible sandpile on the unit torus converges to a bi-Laplacian field. Here, we study α -long-range divisible sandpiles, similar to those introduced in Frómeta and Jara (2018). We show that, for α ∈ (0 , 2) , the limiting field is a fractional Gaussian field on the torus with parameter α / 2. However, for α ∈ [ 2 , ∞) , we recover the bi-Laplacian field. This provides an alternative construction of fractional Gaussian fields such as the Gaussian Free Field or membrane model using a diffusion based on the generator of Lévy walks. The central tool for obtaining our results is a careful study of the spectrum of the fractional Laplacian on the discrete torus. More specifically, we need the rate of divergence of the eigenvalues as we let the side length of the discrete torus go to infinity. As a side result, we obtain precise asymptotics for the eigenvalues of discrete fractional Laplacians. Furthermore, we determine the order of the expected maximum of the discrete fractional Gaussian field with parameter γ = min { α , 2 } and α ∈ R + ∖ { 2 } on a finite grid. [ABSTRACT FROM AUTHOR]

We study cocycles of countable groups of Borel automorphisms of a standard Borel space (X,B) taking values in a locally compact second countable group G. We prove that for a hyperfinite group - the subgroup of coboundaries is dense in the group of cocycles. We describe all Borel cocycles of the 2-odometer and show that any such cocycle is cohomologous to a cocycle with values in a countable dense subgroup H of G. We also provide a Borel version of Gottschalk-Hedlund theorem. [ABSTRACT FROM AUTHOR]

Positioning accuracy is becoming more and more important as autonomous vehicle technology develops. The focus of this paper is on an onboard smart mobile device's feature to improve positioning accuracy, based on experimentally acquired GPS and odometer sensor data. A simplificative odometer reading based approach is applied instead of using more advanced smart device sensors (e.g., accelerometer, gyroscope). Numerous driving tests were performed and analyzed to collect sufficient dynamic travel data. Traveled distances between two consecutive positions from GPS data are computed and correlated with a vehicle's speed profile between the same two positions. To calculate distance more precisely speed values from GPS are corrected with odometer sensor reading. The results revealed an average increase in accuracy of 20%. The developed model can be incorporated with a smart device's other low-energy sensors. Using the smart device sensors, the developed model can be extended to acquire a more accurate positioning. [ABSTRACT FROM AUTHOR]

This work deals with the development of an adaptive multisensor data fusion technique for the accurate estimation of the trains position and velocity. The proposed technique will work with the Train Collision Avoidance System (TCAS) used in Indian railways during Global Positioning System (GPS) outages. The determination of accurate position of trains is a challenging task for the TCAS during GPS outages. The accuracy of the proposed Volterra Recursive Least Square (VRLS) based adaptive multisensor data fusion technique is evaluated by generating two kinematic profiles for a passenger train running between Silchar–Lumding broad gauge route in Indian railways. The effect of accelerometer bias is also considered during the analysis. It is observed that the developed technique can provide a better estimate of the position and velocity for the TCAS especially during GPS outages and without using any additional railway infrastructure. The simulation results indicate that the proposed technique is superior to the earlier works in terms of achieving better positional accuracy in presence of accelerometer bias.

In the field of transportation and logistics, smart vision systems have been employed successfully to automate various tasks such as number-plate recognition and vehicle identity recognition. The development of such automated systems is possible with the availability of large image datasets having proper annotations. The TRODO dataset is a rich-annotated collection of odometer displays that can enable automatic mileage reading from raw images. Initially, the dataset consisted of 2613 frames captured in different conditions in terms of resolution, quality, illumination and vehicle type. After data pre-processing and cleaning, the number of images was reduced to 2389. The images were annotated using the CVAT image annotation tool. The dataset provides the following information for each frame: the type of odometer (analog or digital), the mileage value displayed on the odometer, the bounding boxes of the odometer, and the digits and characters displayed on the screen. Combined with machine learning and artificial intelligence, the TRODO dataset can be used to train odometer classifiers, digit recognition and number reading models from odometers and similar types of displays.

Since 23 June 2020, BDS-3 has been entirely operated and obtained the ability of global PNT (Positioning, Navigation, and Timing) services. Afterward, real-time Precise Point Positioning (PPP) service is available in China’s surrounding areas via BDS-3 PPP-B2b signal. However, such a real-time PPP service cannot maintain the high accuracy and continuity of positioning solutions in challenging environments, such as urban environments. For that, we carried out a model by integrating between-satellite single-differenced (BSSD) PPP, a low-cost Inertial Navigation System (INS), and an odometer via an extended Kalman filter. The performance of this integration model was assessed with vehicle-borne data. Results demonstrated that (1) the position RMS (Root Mean Square) of BSSD PPP are 64.33 cm, 53.47 cm, and 154.11 cm. Compared with BSSD PPP, about 31.2%, 23.3%, and 27.3% position improvements can be achieved by using INS. Further enhancements of position RMS benefiting from the odometer are 1.34%, 1.41%, and 1.73% in the three directions. (2) Anyway, the accuracy of BSSD PPP/INS/Odometer tightly coupled integration is slightly higher than that of undifferenced PPP/INS/Odometer integration, with average improvement percentages of 7.71%, 3.09%, and 0.27%. Meanwhile, the performance of BSSD PPP/INS/Odometer integration during the periods with satellite outages is better than the undifferenced PPP-based solutions. (3) The improvements in attitudes from an odometer are more significant on heading angle than the other two attitudes, with percentages of 25.00%. (4) During frequent GNSS outage periods, the reduction in average maximum position drifts provided by INS are 18.01%, 8.95%, and 20.74%. After integrating with an odometer, the drifts can be furtherly decreased by 25.11%, 15.96%, and 20.69%. For attitude, about 41.67% reduction in average maximum drifts of heading angles is obtained.

In the traditional shearer-positioning method, an odometer is used to assist the forward velocity correction of the inertial navigation system, but it cannot restrain the system’s error divergence. For this reason, this paper proposes a shearer positioning method based on non-holonomic constraints. In this method, an inertial measurement unit and odometer are installed in the middle of the shearer body and on the traction gear, respectively, the shearer attitude, speed, and position information are calculated through the inertial measurement-unit mechanization, and the shearer’s instantaneous velocity is calculated through the output of the odometer. The mechanization and error transfer process of the inertial navigation system are used to establish a Kalman filtering-state equation. The Kalman filtering observation equation is established through the difference between the projected velocity of the inertial navigation system at the joint and the output velocity of the odometer as the observation vector, and the non-holonomic constraint is introduced. Finally, the error feedback is derived from the results processed by the Kalman filtering algorithm, and the output of the inertial navigation system is corrected to obtain the optimal estimation of the shearer’s attitude, speed and position. The experiment shows that compared with the traditional inertial navigation and odometer combined positioning method, the degree of divergence in the positioning results over time is significantly reduced after adding the non-holonomic constraint. The positioning method has good tracking ability for the trajectory of the shearer. The error of the positioning results in the forward direction is reduced by 66%, the lateral direction is reduced by 62%, and the vertical direction is reduced by 67%.