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In this paper, we give explicit conditions characterizing the Følner rank one group actions that factor onto a finite odometer; those that factor onto an arbitrary, but specified odometer, and those that factor onto an unspecified odometer. We also give explicit conditions describing the Følner rank one actions that are conjugate to a specific odometer, and those that are conjugate to some odometer. These conditions are based on cutting and stacking procedures used to generate the action, and generalize results given by Foreman, Gao, Hill, Silva and Weiss for rank one Z -actions. [ABSTRACT FROM AUTHOR]

Summary: Imagine being tasked with measuring the distance between two points in a city, but you can only use a taxicab's odometer which displays only whole-number values (the integer part) of its distance traveled so far—such is the display of modern digital measuring devices. This coarse measurement between two points might be called, tongue in cheek, a taxicab metric, but clearly of a different sort from the taxicab metric on a discrete grid that we encounter in combinatorics. We will explore how we might apply probability to our taxicab's inherent randomness, and along with limit theorems, indeed measure arbitrary distances with any desired accuracy. [ABSTRACT FROM AUTHOR]

High-precision positioning is a fundamental requirement for autonomous vehicles. However, the accuracy of single-sensor positioning technology can be compromised in complex scenarios due to inherent limitations. To address this issue, we propose an adaptive multi-sensor fusion localization method based on the error-state Kalman filter. By incorporating a tightly coupled laser inertial odometer that utilizes the Normal Distribution Transform (NDT), we constructed a multi-level fuzzy evaluation model for posture transformation states. This model assesses the reliability of Global Navigation Satellite System (GNSS) data and the laser inertial odometer when GNSS signals are disrupted, prioritizing data with higher reliability for posture updates. Real vehicle tests demonstrate that our proposed positioning method satisfactorily meets the positioning accuracy and robustness requirements for autonomous driving vehicles in complex environments. [ABSTRACT FROM AUTHOR]

The autonomous navigation of mobile robots contains three parts: map building, global localization, and path planning. Precise pose data directly affect the accuracy of global localization. However, the cumulative error problems of sensors and various estimation strategies cause the pose to have a large gap in data accuracy. To address these problems, this paper proposes a pose calibration method based on localization and point cloud registration, which is called L-PCM. Firstly, the method obtains the odometer and IMU (inertial measurement unit) data through the sensors mounted on the mobile robot and uses the UKF (unscented Kalman filter) algorithm to filter and fuse the odometer data and IMU data to obtain the estimated pose of the mobile robot. Secondly, the AMCL (adaptive Monte Carlo localization) is improved by combining the UKF fusion model of the IMU and odometer to obtain the modified global initial pose of the mobile robot. Finally, PL-ICP (point to line-iterative closest point) point cloud registration is used to calibrate the modified global initial pose to obtain the global pose of the mobile robot. Through simulation experiments, it is verified that the UKF fusion algorithm can reduce the influence of cumulative errors and the improved AMCL algorithm can optimize the pose trajectory. The average value of the position error is about 0.0447 m, and the average value of the angle error is stabilized at about 0.0049 degrees. Meanwhile, it has been verified that the L-PCM is significantly better than the existing AMCL algorithm, with a position error of about 0.01726 m and an average angle error of about 0.00302 degrees, effectively improving the accuracy of the pose. [ABSTRACT FROM AUTHOR]

The traditional laser SLAM (Simultaneous Localization and Mapping) algorithm uses the global relative poses and local ones to form residual blocks. Its constructed map is not smooth enough and the constraint construction is too simplex under some special scenarios. Thus, this paper proposes an odometer constraint fusion method called FOSLAM (Fusion Odometer SLAM) to construct residual blocks between constrains and solve the nonlinear least squares by Ceres. The effectiveness and accuracy of this method have been verified through comparative experiments. Experimental results showed that without increasing the time and space complexity, by involving the odometer constraint into the SLAM optimization process, the convergence of scan matching scores can be improved and the constructed grid map edges are smoother and the jagged phenomenon can be reduced. Under sophisticated scene, FOSLAM is able to acquire more accurate maps and laser odometer trajectory than Cartographer method. Therefore, it is suitable to be used on indoor robot for cleaning and inspection and can be further deployed on autonomous unmanned vehicles involving spatial visualization and neuro-heuristic guidance. [ABSTRACT FROM AUTHOR]

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]

In order to solve the problem of large positioning error and incomplete mapping of SLAM based on two-dimensional lidar in indoor environment, a multi-sensor fusion SLAM algorithm for indoor robots was proposed. Aiming at the mismatch problem of the traditional ICP algorithm in the front end of the lidar SLAM, the algorithm adopts the PL-ICP algorithm that is more suitable for the indoor environment, and uses the extended Kalman filter to fuse the wheel odometer and IMU to provide the initial motion estimation value. Then, during the mapping phase, the pseudo 2D laser data converted from the 3D point cloud data obtained by the depth camera is fused with the data obtained from the 2D lidar to compensate for the lack of vertical field of view in the 2D lidar mapping. The final experimental results show that the fusion odometer data has improved the positioning accuracy by at least 33% compared to a single wheeled odometer, providing a higher initial iteration value for the PL-ICP algorithm. At the same time, fusion mapping compensates for the shortcomings of a single two-dimensional lidar mapping, and constructs an environmental map with more complete environmental information. [ABSTRACT FROM AUTHOR]

In 1932 von Neumann proposed classifying the statistical behavior of differentiable systems. In modern language this is interpreted as classifying diffeomorphisms of compact manifolds up to measure isomorphism. This paper proves that this is impossible in a rigorous sense.

Ultra-wideband (UWB) systems promise centimetre-level accuracy for indoor positioning, yet they remain susceptible to non-line-of-sight (NLOS) errors due to complex indoor environments. A fusion mechanism that integrates the UWB with an odometer sensor is introduced to address this challenge and achieve a high positioning accuracy. A sliding window method is applied to identify NLOS anchors effectively. The modified UWB-only positioning has an average error under 13 cm with an RMSE of 16 cm. Then, a loosely coupled approach named Dynamic Dimension Fusion (DDF) is designed to mitigate the odometer's cumulative errors that achieve a remarkable average error and RMSE below 5 cm, notably superior to established unscented Kalman filter (UKF) fusion techniques. DDF utilises UWB data to correct the one-dimensional heading error of the odometer when the robot moves in a straight line and to correct both heading and mileage in two dimensions when the robot is turning. Comprehensive real-world experimental evaluations underscore the efficacy and robustness of this novel approach. [ABSTRACT FROM AUTHOR]

Rollers, integral to road construction, are undergoing rapid advancements in unmanned functionality. To address the specific challenge of unmanned compaction within tunnels, we propose a vision-based odometry system for unmanned rollers. This system solves the problem of tunnel localization under conditions of low texture and high noise. We evaluate and compare the performance of various feature extraction and matching methods, followed by the application of random sample consensus (RANSAC) to eliminate false matches. Subsequently, Perspective-n-Points (PnP) was employed to establish a minimal-error analysis for pose estimation and trajectory analysis. The findings reveal that binary robust invariant scalable key points (BRISK) exhibits larger errors due to fewer correctly matched feature points, while scale invariant feature transform (SIFT) falls short of real-time requirements. Compared to Oriented FAST and Rotated BRIEF (ORB) and the direct method, the maximum relative error and the median error between the compaction trajectory estimated by speed-up robust features (SURF) and the actual trajectory were the smallest. Consequently, the unmanned rollers employing SURF + PnP improved the accuracy and robustness. This research contributes valuable insights to the development of autonomous road construction equipment, particularly in challenging tunnels. [ABSTRACT FROM AUTHOR]

To improve the reliability of strapdown inertial navigation system (SINS)/Doppler radar/odometer integrated navigation system, the federated Kalman filter with two-stage fault detection structure is designed, and a fault-tolerant SINS/Doppler radar/odometer integrated navigation method is proposed. Firstly, the pre-fault detection module sets before the local filter, and the residual chi-square test in the carrier coordinate system is selected to detect the abrupt faults of Doppler radar and odometer. Then, the secondary-fault detection module emplaces between the local filter and the main filter, and the sequential probability ratio test (SPRT) is selected to further detect the ramp faults that are difficult to detect by the residual chi-square test. To address the limitation of the SPRT in accurately determining the end time of faults, an improved SPRT is proposed. The improved SPRT reduces the influence of historical fault on the fault statistics by introducing forgetting factors to improve its sensitivity to the fault end. The simulation experiment indicates that the proposed method can quickly detect and isolate abrupt and ramp faults, and promptly restore normal operation of the integrated navigation system after the fault ends, effectively improving the fault tolerance and reliability of the integrated navigation system. [ABSTRACT FROM AUTHOR]

Copyright of Railway Signalling & Communication Engineering is the property of Railway Signalling & Communication Engineering 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.)

We describe the infinite interval exchange transformations, called the rotated odometers, which are obtained as compositions of finite interval exchange transformations and the von Neumann–Kakutani map. We show that with respect to Lebesgue measure on the unit interval, every such transformation is measurably isomorphic to the first return map of a rational parallel flow on a translation surface of finite area with infinite genus and a finite number of ends. We describe the dynamics of rotated odometers by means of Bratteli–Vershik systems, derive several of their topological and ergodic properties, and investigate in detail a range of specific examples of rotated odometers. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics 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.)

Internal diffusion limited aggregation (IDLA) is a random aggregation model on a graph G, whose clusters are formed by random walks started in the origin (some fixed vertex) and stopped upon visiting a previously unvisited site. On the Sierpinski gasket graph, the asymptotic shape is known to be a ball in the graph metric. In this paper, we improve the sublinear bounds for the fluctuations known from its known asymptotic shape result by establishing bounds for the odometer function for a divisible sandpile model. [ABSTRACT FROM AUTHOR]

Consider a stationary process taking values in a finite state space. Each state is associated with a finite one-shot zero-sum game. We investigate the infinitely repeated zero-sum game with incomplete information on one side in which the state of the game evolves according to the stationary process. Two players, named the observer and the adversary, play the following game. At the beginning of any stage, only the observer is informed of the state ξn and is therefore the only one who knows the identity of the forthcoming one-shot game. Then, both players take actions, which become publicly known. The paper shows the existence of a uniform value in a new class of stationary processes: ergodic Kronecker systems. Techniques from ergodic theory, probability theory, and game theory are employed to describe the optimal strategies of the two players. Funding: This work was supported by the Israel Science Foundation [Grant 591/21] and Deutsche Forschungsgemeinschaft [Grant KA 5609/1-1]. [ABSTRACT FROM AUTHOR]

Copyright of Piezoelectrics & Acoustooptics is the property of Piezoelectric & Acoustooptic 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 reduce the error of the odometer positioning system and improve the positioning accuracy of four-wheel-steering mobile robots, three types of coupling errors are considered, based on the University of Michigan Benchmark (UMBmark) method: unequal track width, unequal wheel diameter, and speed difference of ipsilateral wheels. A "dual direction square path experiment" is designed to decouple the error, a new system error model is defined, and an improved UMBmark method for a four-wheel mobile robot is proposed. In the mobile robot positioning system, a laser tracker is used to measure the absolute positions of the starting and ending points of the robot. The positioning test results of the robot using the improved UMBmark method show that the odometer system error is 69.103 mm, which is 2.6 times less than that in the traditional UMBmark method. Hence, the improved UMBmark can better compensate for the system error of four-wheel-steering mobile robots. [ABSTRACT FROM AUTHOR]

The main result of this paper is that two large collections of er-godic measure preserving systems, the Odometer Based and the Circular Systems have the same global structure with respect to joinings that preserve underlying timing factors. The classes are canonically isomorphic by a contin-uous map that takes synchronous and anti-synchronous factor maps to synchronous and anti-synchronous factor maps, synchronous and anti-synchro-nous measure-isomorphisms to synchronous and anti-synchronous measure-isomorphisms, weakly mixing extensions to weakly mixing extensions and compact extensions to compact extensions. The first class includes all finite entropy ergodic transformations that have an odometer factor. By results in [6], the second class contains all transformations realizable as dif-feomorphisms using the untwisted Anosov–Katok method. An application of the main result will appear in a forthcoming paper [7] that shows that the diffeomorphisms of the torus are inherently unclassifiable up to measure-isomorphism. Other consequences include the existence of measure distal diffeomorphisms of arbitrary countable distal height.

The article focuses on the ongoing controversy surrounding Mandyam Veerambudi "Srini" Srinivasan's research into honey bee behavior, particularly their waggle dance communication. It is reported that the research led by Laura Luebbert and researchers from Caltech (California Institute of Technology), and raises concerns about potential data manipulation, image reuse, and methodological inconsistencies across multiple studies published between 1996 and 2010.

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]

We show that Cantor minimal Z x Z2-systems and essentially free amenable odometers are almost finite. We also compute the homology groups of Cantor minimal Z x Z2-systems and show that the associated transformation groupoids satisfy the HK conjecture if and only if the action is free. [ABSTRACT FROM AUTHOR]

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]

Diam-mean equicontinuity is a dynamical property that has been of use in the study of non-periodic order. Using some type of 'local' skew product between a shift and an odometer looking cellular automaton (CA), we will show that there exists an almost diam-mean equicontinuous CA that is not almost equicontinuous (and hence not almost locally periodic). Previously, we constructed a CA that is almost mean equicontinuous [L.D.I.S. Baños and F. García-Ramos, Mean equicontinuity and mean sensitivity on cellular automata, Ergodic Theory Dynam. Systems 41 (12) (2021), pp. 3704–3721] but not almost diam-mean equicontinuous [L.D.I.S. Baños and F. García-Ramos, Diameter mean equicontinuity and cellular automata, Proceedings of the 27th International Workshop on Cellular Automata and Discrete Complex Systems, arXiv:2106.09641, 2021]. [ABSTRACT FROM AUTHOR]

Localization for multi-target systems (MTS) in anchor-free scenarios is receiving increasing attention, particularly for MTS, such as the Internet of Vehicles and swarms. This paper proposes a state-of-the-art fusion model based on the odometer and ranging measurements to address the anchorfree localization problem for MTS. Regarding varying measurements as a topological structure of the position, we formulate the optimization problem as a Procrustes problem that fully utilizes measurement information. Singular value decomposition is used to obtain the closed-form solution to the problem, which places minimal demands on computational resources. We also introduce an adaptive mechanism with a trade-off factor to enhance the model's flexibility. Additionally, the model applies to 2D and 3D cases and is generally suitable for most odometer and ranging technologies. Simulation experiments demonstrate that the positioning accuracy of the proposed scheme significantly improves over conventional optimizationbased methods and outperforms the state-of-the-art. [ABSTRACT FROM AUTHOR]

We introduce substitutions in Z m which have non-rectangular domains based on an endomorphism Q of Z m and a set D of coset representatives of Z m / Q Z m , which we call digit substitutions. Using a finite abelian 'spin' group we define 'spin digit substitutions' and their subshifts (Σ , Z m). Conditions under which the subshift is measure-theoretically isomorphic to a group extension of an m -dimensional odometer are given, inducing a complete decomposition of the function space L 2 (Σ , μ). This enables the use of group characters in G ^ to derive substitutive factors and analyze the spectra of specific subspaces. We provide general sufficient criteria for the existence of pure point, absolutely continuous, and singular continuous spectral measures, together with some bounds on their spectral multiplicity. [ABSTRACT FROM AUTHOR]

Strapdown inertial navigation usually relies on the extended Kalman filter (EKF) as the workhorse, which requires a proper initialization of the attitude. The optimization-based alignment (OBA) approach is widely employed to provide the coarse attitude information for the subsequent fine alignment or navigation stage by EKF. However, there still lacks a reliable quality index to assess the attitude accuracy of OBA. To tackle this problem, this article characterizes the OBA attitude error by vector measurement errors. For specific applications, such as the stationary alignment and odometer/GPS-aided in-motion alignment, the errors of vector measurements in OBA are further formulated in terms of raw sensor errors of gyroscopes, accelerometers, and odometer/GPS velocities. The covariance of attitude errors is analytically derived. Simulations and field experiments are performed to verify the covariance analysis. [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.)

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]

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. [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.)

Global navigation satellite system (GNSS) and inertial navigation system (INS) are indispensable for ground vehicle position and navigation. The Kalman filter (KF) is the first choice to integrate them and output more reliable navigation solutions. However, the GNSS signal is denied in urban areas, i.e., tunnels, and the INS position errors diverge quickly over time. Under normal conditions, the ground vehicle will not slide or jump off the ground; nonholonomic constraints (NHC) and odometers are available to aid the INS and reduce its position errors. Factor graph optimization (FGO) recently attracted attention as an advanced sensor fusion algorithm. This paper implemented the FGO method based on GNSS/INS/NHC/Odometer integration. In the FGO, state transformation, measurement model, the NHC, and the odometer were all regarded as constraints employed to construct a graph; an iterative process was utilized to find the optimal estimation results. Two experiments were carried out: firstly, the FGO-GNSS/INS performance was assessed and compared with the KF-GNSS/INS; secondly, we compared the FGO-GNSS/INS/NHC/Odometer and KF-GNSS/INS/NHC/Odometer under GNSS denied environments. Experimental results supported that the FGO improved the performance. [ABSTRACT FROM AUTHOR]

In 1932 von Neumann proposed classifying the statistical behavior of differentiable systems. In modern language this is interpreted as classifying diffeomorphisms of compact manifolds up to measure isomorphism. This paper proves that this is impossible in a rigorous sense. [ABSTRACT FROM AUTHOR]

Weighted shifts are an important concrete class of operators in linear dynamics. In particular, they are an essential tool in distinguishing a variety of dynamical properties. Recently, a systematic study of dynamical properties of composition operators on L^p spaces has been initiated. This class of operators includes weighted shifts and also allows flexibility in construction of other concrete examples. In this article, we study one such concrete class of operators, namely composition operators induced by measures on odometers. In particular, we study measures on odometers which induce mixing and transitive linear operators on L^p spaces. [ABSTRACT FROM AUTHOR]

An odometer wheel is used to measure forward distance for a piezoelectric ultrasonic device, and it plays a key role in locating defections. Buckling and skidding are its main problems in applications. Based on a self-developed in-line inspection tool, an odometer wheel was designed according to mechanical design principles. A finite element model on scale of 1 : 1 was built to simulate mechanical performance of the odometer wheel. Results show that the maximum Mises stress under the worst condition is far less than the elastic limit of chosen stainless steel, and deformations of swing arms are slight. Acting forces under different conditions also meet requirements of antiskid. It indicates that the designed object has good performance in terms of structure stability and skid prevention. [ABSTRACT FROM AUTHOR]

Quadruped robots, an important class of unmanned aerial vehicles, have broad potential for applications in education, service, industry, military, and other fields. Their independent positioning plays a key role for completing assigned tasks in a complex environment. However, positioning based on global navigation satellite systems (GNSS) may result in GNSS jamming and quadruped robots not operating properly in environments sheltered by buildings. In this paper, a tightly coupled LiDAR vision inertial odometer (LVIO) is proposed to address the positioning inaccuracy of quadruped robots, which have poor mileage information obtained though legs and feet structures only. With this optimization method, the point cloud data obtained by 3D LiDAR, the image feature information obtained by binocular vision, and the IMU inertial data are combined to improve the precise indoor and outdoor positioning of a quadruped robot. This method reduces the errors caused by the uniform motion model in laser odometer as well as the image blur caused by rapid movements of the robot, which can lead to error-matching in a dynamic scene; at the same time, it alleviates the impact of drift on inertial measurements. Finally, the quadruped robot in the laboratory is used to build a physical platform for verification. The experimental results show that the designed LVIO effectively realizes the positioning of four groups of robots with high precision and strong robustness, both indoors or outdoors, which verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

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]

We study minimal Z d -Cantor systems and the relationship between their speedups, their collections of invariant Borel measures, their associated unital dimension groups, and their orbit equivalence classes. In the particular case of minimal Z d -odometers, we show that their bounded speedups must again be odometers but, contrary to the 1-dimensional case, they need not be conjugate, or even isomorphic, to the original. Furthermore, we give examples of speedups of Z d -odometers which show the significant role played by a choice of 'cone' associated to the speedup. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hebei University of Science & Technology is the property of Hebei University of Science & Technology, Journal of Hebei University of Science & Technology 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.)

We establish a Wold-type decomposition for isometric and isometric Nica-covariant representations of the odometer semigroup. These generalize the Wold-type decomposition for commuting pairs of isometries due to Popovici and for pairs of doubly commuting isometries due to Słociński. [ABSTRACT FROM AUTHOR]

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]

Most of the current tunnel defect detection methods do not perform repeated inspection of the defects taken at different times at the same location, which is a major deficiency for detecting the development trend of the defects. To solve the problem of repeated inspection of tunnel lining defects, this paper proposes a new method based on the combination of enhanced visual odometer and a Sequence-Siamese network(Seq-Siamese-Net) to identify and locate tunnel lining defects. We first detect the defects in the tunnel lining surface video by You Only Look Once network, then estimate the general location of the defects by using the proposed enhanced visual odometer, and finally obtain the most matching defects in the vicinity of the defects by Seq-Siamese-Net to achieve the repeated inspection of the tunnel lining surface defects. Experiments show that our proposed enhanced visual odometer method outperforms the original visual odometer method in terms of localization accuracy, while the feature extracted form Seq-Siamese-Network is more effective than the average-pooled feature in the tunnel defect image sequence matching task. Furthermore, experiments in a real tunnel demonstrate the practical value of the proposed method. • A network-based method for repeated detection of tunnel lining defects is proposed. • Average error of localization method reached about 1% at real tunnel experiment. • Sequence-Siamese-Network surpasses averaged feature in image sequence matching task. [ABSTRACT FROM AUTHOR]

Determination of calibration parameters is essential for the fusion performance of an inertial measurement unit (IMU) and odometer integrated navigation system. Traditional calibration methods are commonly based on the filter frame, which limits the improvement of the calibration accuracy. This paper proposes a graph-optimisation-based self-calibration method for the IMU/odometer using preintegration theory. Different from existing preintegrations, the complete IMU/odometer preintegration model is derived, which takes into consideration the effects of the scale factor of the odometer, and misalignments in the attitude and position between the IMU and odometer. Then the calibration is implemented by the graph-optimisation method. The KITTI dataset and field experimental tests are carried out to evaluate the effectiveness of the proposed method. The results illustrate that the proposed method outperforms the filter-based calibration method. Meanwhile, the performance of the proposed IMU/odometer preintegration model is optimal compared with the traditional preintegration models. [ABSTRACT FROM AUTHOR]

Autonomous navigation without external GNSS aiding is crucial for some kinds of land vehicle applications, such as military vehicles and unmanned vehicles navigation under GNSS denied environments. A typical solution for autonomous navigation can be achieved by integrating Inertial Navigation System (INS) and odometer, where the odometer can provide velocity aiding for INS. The INS/odometer integration approach can dramatically improve the navigation performances compared with the standalone INS approach, however its positioning error is still gradually accumulating with time because of lacking external position correction. This paper proposes an approach to aid the INS/odometer integration by using vision positioning, where a UAV is used to carry the vision camera and helps to realize the positioning of the vehicle by image matching. The UAV vision positioning acts a role like GNSS and provides constant position correction for INS/odometer integration. A dual-rate Kalman filter is proposed and utilized to realize the data fusing of vision, INS and odometer. Simulation and filed tests show that the proposed approach can dramatically improve the autonomous navigation performances for land vehicles. [ABSTRACT FROM AUTHOR]

In the visual positioning of Unmanned Ground Vehicle (UGV), the visual odometer based on direct sparse method (DSO) has the advantages of small amount of calculation, high real-time performance and high robustness, so it is more widely used than the visual odometer based on feature point method. Ordinary vision sensors have a narrower viewing angle than panoramic vision sensors, and there are fewer road signs in a single frame of image, resulting in poor road sign tracking and positioning capabilities, and severely restricting the development of visual odometry. Based on these considerations, this paper proposes a binocular stereo panoramic vision positioning algorithm based on extended DSO, which can solve these problems well. The experimental results show that the binocular stereo panoramic vision positioning algorithm based on the extended DSO can directly obtain the panoramic depth image around the UGV, which greatly improves the accuracy and robustness of the visual positioning compared with other ordinary visual odometers. It will have widely application prospects in the UGV field in the future. [ABSTRACT FROM AUTHOR]

It is well understood that in ADAS applications, a good estimate of the pose of the vehicle is required. This paper proposes a metaphorically named 2.5D odometry, whereby the planar odometry derived from the yaw rate sensor and four wheel speed sensors is augmented by a linear model of suspension. While the core of the planar odometry is a yaw rate model that is already understood in the literature, this is augmented by fitting a quadratic to the incoming signals, enabling interpolation, extrapolation, and a finer integration of the vehicle position. It is shown, by experimental results with a DGPS/IMU reference, that this model provides highly accurate odometry estimates, compared with existing methods. Utilising sensors that return the change in height of vehicle reference points with changing suspension configurations, a planar model of the vehicle suspension is defined, thus augmenting the odometry model. An experimental framework and evaluations criteria is presented by which the goodness of the odometry is evaluated and compared with existing methods. This odometry model has been designed to support low‐speed surround‐view camera systems that are well‐known. Thus, some application results that show a performance boost for viewing and computer vision applications using the proposed odometry are presented. [ABSTRACT FROM AUTHOR]

Root piles are injected and installed during mortar shaft construction, using pressures of up to 500 kPa. The executive control is typically done through static load testing, an expensive and time-consuming method. Static load tests on eight controlled piles (diameters between 310 mm and 410 mm) were performed, aiming at evaluating pile ultimate load. This study suggests an innovative, non-destructive approach to validate root pile field performance, using a digital speedometer connected to the drilling rig's rotating head. The proposed method monitors variables related to bearing capacity during pile installation and proposes an empirical equation to estimate the ultimate bearing capacity of root piles. For the assessed piles, the predictions obtained with the proposed equations agreed fairly well with results from static load tests, proving it as a feasible and helpful option for the executive control of root piles, especially when load tests are not available. [ABSTRACT FROM AUTHOR]