Global Navigation Satellite System Information Research Articles

Uncertainty‐aware LiDAR‐based localization for outdoor mobile robots

AbstractAccurate and robust localization is essential for autonomous mobile robots. Map matching based on Light Detection and Ranging (LiDAR) sensors has been widely adopted to estimate the global location of robots. However, map‐matching performance can be degraded when the environment changes or when sufficient features are unavailable. Indiscriminately incorporating inaccurate map‐matching poses for localization can significantly decrease the reliability of pose estimation. This paper aims to develop a robust LiDAR‐based localization method based on map matching. We focus on determining appropriate weights that are computed from the uncertainty of map‐matching poses. The uncertainty of map‐matching poses is estimated by the probability distribution over the poses. We exploit the normal distribution transform map to derive the probability distribution. A factor graph is employed to combine the map‐matching pose, LiDAR‐inertial odometry, and global navigation satellite system information. Experimental verification was successfully conducted outdoors on the university campus in three different scenarios, each involving changing or dynamic environments. We compared the performance of the proposed method with three LiDAR‐based localization methods. The experimental results show that robust localization performances can be achieved even when map‐matching poses are inaccurate in various outdoor environments. The experimental video can be found at https://youtu.be/L6p8gwxn4ak.

Cooperative Localization With the Fusion of GNSS and Relative Range Information in Vehicular Ad-Hoc Networks

Accurate vehicle position is crucial to vehicular Ad-Hoc network (VANET) applications. The wildly used global navigation satellite systems (GNSS) have limited localization accuracy, especially when GNSS signals are blocked or contaminated with reflected signals. Cooperative localization based on multi-vehicle information fusion will be the core component in VANET in the near future. This paper strives to enhance the localization accuracy using the fusion of GNSS and relative range information in static scenes. The formulation following the Maximum-Likelihood strategy is a quadratic non-convex programming, which is NP-hard. An iterative localization algorithm is proposed, where an approximate quadratic programming with linear constraints is solved in each iteration. Additional inspection steps and correction step are designed to ensure the convergence and feasibility of the proposed algorithm. The theoretical analysis shows that the limit of the proposed algorithm must be a global optimal solution of the formulated problem and the mean squared error can reach the Cramér-Rao lower bound. In terms of localization accuracy, the proposed method outperforms the existing linearized weighted least-squares method and semidefinite relaxation method, while exhibiting moderate computational complexity.

Parameterisation of the GNSS troposphere tomography domain with optimisation of the nodes’ distribution

Water vapour is a highly variable constituent of the troposphere; thus, its high-resolution measurements are of great importance to weather prediction systems. The Global Navigation Satellite Systems (GNSS) are operationally used in the estimation of the tropospheric state and assimilation of the results into the weather models. One of the GNSS techniques of troposphere sensing is tomography which provides 3-D fields of wet refractivity. The tomographic results have been successfully assimilated into the numerical weather models, showing the great potential of this technique. The GNSS tomography can be based on two different approaches to the parameterisation of the model’s domain, i.e. block (voxel-based) or grid (node-based) approach. Regardless of the parameterisation approach, the tomographic domain should be discretised, which is usually performed in a regular manner, with a grid resolution depending on the mean distance between the GNSS receivers. In this work, we propose a new parameterisation approach based on the optimisation of the tomographic nodes’ location, taking into account the non-uniform distribution of the GNSS information in the troposphere. The experiment was performed using a dense network of 16 low-cost multi-GNSS receivers located in Wrocław and its suburbs, with a mean distance of 3 km. Cross-validation of four different parameterisation approaches is presented. The validation is performed based on the Weather Research and Forecasting model as well as radiosonde observations. The new approach improves the results of wet refractivity estimation by 0.5–2 ppm in terms of RMSE, especially for altitudes of 0.5–2.0 km.

Variable Observability Constrained Visual-Inertial-GNSS EKF-Based Navigation

Visual-Inertial Odometry (VIO) is an approach to give high-accuracy pose estimation in GNSS(Global Navigation Satellite System)-denied environments. However, visual-inertial navigation slowly drifts on its four unobservable directions, namely the translations and yaw in the world frame. The visual and inertial sensors are unable to provide information about how the world frame is aligned with global geographic coordinates. In this letter, g-MSCKF, a Multi-State Constraint Kalman Filter based approach, is developed to combine visual, inertial and GNSS raw measurements. With GNSS raw measurements, g-MSCKF has the ability to use GNSS information even when the number of satellites is below 4. An alignment filter between the local world frame and the global geographic frame is proposed and serves as the initializer for g-MSCKF. Furthermore, unobservable directions may exist and vary when the number of satellites, the satellite-receiver spatial geometry and the receiver motion follow specific patterns. Inconsistency of the estimator may happen under those variable-unobservable circumstances and a variable observability constrained method is provided to avoid inconsistency. Our algorithm is evaluated on real-world open datasets where the sensors traverse indoors and outdoors. The results show that our solution gives globally smooth trajectories in GNSS-intermittent situations with full exploitation of sensor potentials.

Automatic geo‐localization framework without GNSS data

The information unavailability of a global navigation satellite system (GNSS) for geo-localization in many military and tourism applications implies the need to employ other applicable types of information, such as automatic image-based location recognition. Unlike most existing localization methods that use GNSS information to locate an initial position, this paper proposes an automatic geo-localization framework that does not require GNSS data. The proposed framework is a two-stage pipeline that uses a query image and digital elevation model (DEM) data as input. The authors frame automatic geo-localization without GNSS recordings as a skyline matching problem. By extracting the skyline from the DEM data and query images, the query image can be localized by matching the query skyline feature to the DEM skyline database. It has been demonstrated that this low-cost approach can perform efficiently in mountainous or hilly areas to produce reliable localization results. The system was tested on 50 testing site points within a large-scale area (China, 202.6 km2), and an average position error of 43.13 m was detected within 4.5 s.

An Accurate and Real-Time Commercial Indoor Localization System in LTE Networks

In this paper, a commercial high-accuracy, low-cost and real-time indoor building-level localization system is proposed, which is applicable for locating the Minimization of Drive-Tests (MDT) data in the long-term-evolution (LTE) cellular communication network system. The system utilizes MDT data containing Global Navigation Satellite Systems (GNSS) information which is easy to collect and low cost to assist indoor localization, instead of using indoor drive test (DT) data which needs high manual collection and maintenance costs. In order to compensate for the loss of location accuracy, this paper innovatively divide the online process into two phases: indoor and outdoor (IO) identification phase and indoor localization phase. A real-time and precise GMM-based unsupervised algorithm is applied to identifying if the non-GNSS MDT data is in indoor environment in IO identification phase. Then, a multi-class classification algorithm based on Bayesian classifier is used to locate indoor MDT data to the specific building. The results of experiments conducted in an in-service LTE network using more than 100 LTE base stations demonstrate that the proposed technique yields a IO identification accuracy of 90% and an indoor location accuracy of 49.3m(@67%) respectively, which provides at least 30.2% enhancement in location accuracy compared to traditional technology without DT fingerprint database. This proposed indoor localization technique is applicable in network optimization and Operation and Maintenance (O&M) to assist communication service providers to reduce their operating expense (OPEX) by locating those MDT data without GNSS information.

A Real Time Localization System for Vehicles Using Terrain-Based Time Series Subsequence Matching

Global Navigation Satellite Systems (GNSSs) are commonly used for positioning vehicles in open areas. Yet a GNSS frequently encounters loss of lock in urban areas. This paper presents a new real-time localization system using measurements from vehicle odometer data and data from an onboard inertial measurement unit (IMU), in the case of lacking GNSS information. A Dead Reckoning model integrates odometer data, IMU angular and velocity data to estimate the rough position of the vehicle. We then use an R-Tree structured reference road map of pitch data to boost spatial search efficiency. An optimized time series subsequence matching method matches the measured pitch data and the stored pitch data in reference road map for more accurate position estimation. The two estimated positions are fused using an extended Kalman filter model for final localization. The proposed localization system was tested for computational complexity with a median runtime of 12 ms, and for positioning accuracy with a median position error of 0.3 m.

The Design a TDCP-Smoothed GNSS/Odometer Integration Scheme with Vehicular-Motion Constraint and Robust Regression

Global navigation satellite system (GNSS) is widely regarded as the primary positioning solution for intelligent transport system (ITS) applications. However, its performance could degrade, due to signal outages and faulty-signal contamination, including multipath and non-line-of-sight reception. Considering the limitation of the performance and computation loads in mass-produced automotive products, this research investigates the methods for enhancing GNSS-based solutions without significantly increasing the cost for vehicular navigation system. In this study, the measurement technique of the odometer in modern vehicle designs is selected to integrate the GNSS information, without using an inertial navigation system. Three techniques are implemented to improve positioning accuracy; (a) Time-differenced carrier phase (TDCP) based filter: A state-augmented extended Kalman filter is designed to incorporate TDCP measurements for maximizing the effectiveness of phase-smoothing; (b) odometer-aided constraints: The aiding measurement from odometer utilizing forward speed with the lateral constraint enhances the state estimation; the information based on vehicular motion, comprising the zero-velocity constraint, fault detection and exclusion, and dead reckoning, maintains the stability of the positioning solution; (c) robust regression: A weighted-least-square based robust regression as a measurement-quality assessment is applied to adjust the weightings of the measurements adaptively. Experimental results in a GNSS-challenging environment indicate that, based on the single-point-positioning mode with an automotive-grade receiver, the combination of the proposed methods presented a root-mean-square error of 2.51 m, 3.63 m, 1.63 m, and 1.95 m for the horizontal, vertical, forward, and lateral directions, with improvements of 35.1%, 49.6%, 45.3%, and 21.1%, respectively. The statistical analysis exhibits 97.3% state estimation result in the horizontal direction for the percentage of epochs that had errors of less than 5 m, presenting that after the intervention of proposed methods, the positioning performance can fulfill the requirements for road level applications.

Accurate and Direct GNSS/PDR Integration Using Extended Kalman Filter for Pedestrian Smartphone Navigation

According to well-described literature concerning the work history of multipath mitigation in the global navigation satellite systems (GNSS), multipath is still the most dominant factor in a challenging environment. There are unperturbed harsh circumstances where GNSS signals cannot reach and smartphone navigation is not possible. The main objective of this research is to find an accurate solution for pedestrian smartphone navigation in a multipath environment. Experiments are done with micro-electro-mechanical system (MEMS) sensors mounted on a smartphone, and no extra hardware is needed. The latest Android smartphone is used to log the data files of GNSS and MEMS sensors. This scheme has been classified in the synopsis, and a rectangular route with three perpendicular turns has been selected for a pedestrian walk. The data is preprocessed using a low pass filter to remove high-frequency noise and smooth the signal. The description of accumulative error produced by the heading and step size estimation has been reduced by implementing the indices of mean cumulative heading error and cumulative step length error, respectively. In the end, the suboptimal extended Kalman filter algorithm is used to fuse the data of GNSS and pedestrian dead reckoning (PDR) for final results. In this paper, we try to give a technique to provide accurate pedestrian smartphone navigation. The fusion results show that the prospective method explores the possibility to use smartphone navigation in any case when GNSS or PDR information is not available. Substantial simulations are implemented and corroborate that the schemed method is sturdier to use in a harsh environments. The aim is to achieve high-level accuracy with an ultra-low-cost solution.

A Robust Fusion Methodology for MEMS-Based Land Vehicle Navigation in GNSS-Challenged Environments

How to effectively blend global navigation satellite system (GNSS) and inertial navigation system (INS) data to achieve an optimal solution in harsh environments has always been an urgent task. The main challenges for low-cost GNSS/INS integrated land vehicle navigation system are poor accuracy of GNSS observations in complex urban environments and time-growing position errors of stand-alone micro-electro mechanical system (MEMS)-based inertial sensors during GNSS outages. This paper aims to enhance the positioning accuracy and reliability of low-cost integrated system. To attain this, we propose a two-tier robust fusion scheme with different aspects: 1) The GNSS and INS information are fused through a support vector regression-based adapted Kalman filter (SVR-AKF), with which scaling factors are generated to tune the covariance parameters of KF. 2) The position errors of MEMS-INS during GNSS outages are predicted and compensated by modeling INS error characteristics utilizing an adaptive neuro fuzzy inference system (ANFIS) due to its effectiveness in dealing with the nonlinear and uncertainty problems. To verify the feasibility of the proposed methodology, experimental road tests were performed, which suggested that the proposed methodology can significantly improve the overall reliability and positioning performance of land vehicle navigation in GNSS-challenged environments.

Ionospheric Modeling and Forecasting Services of GNSS

The study of ionospheric variability is vital for refining and predicts services of GNSS (Global Navigation Satellite System) applications. The GNSS service provides an explicitly obtainable resource GNSS information, goods and services in support of the earthly reference structure. GNSS is provides an Earth observation, navigation, positioning, timing and other applications that an assistance Society and Science. To investigate the variations in the daily averaged TEC (Total Electron Content) by taking several model components such as solar activity component, geomagnetic activity and periodic components at different latitudes ranging from 10˚N to 26˚N for the year 2018. The presented results would be useful to download, processing and analysis the IGS (International GNSS Service) data. MSSA model can reproduce quite well the observed values of GPS-TEC by utilizing only the first 4 singular modes and constitutes 99% of the total variance. The data is transformed into the singular values which are used for forecasting a noiseless time series. The proposed system results show that higher accuracy is achieved by MSSA (Multivariate Singular Spectrum Analysis) based model. It can also be noted that the training of MSSA is much faster and achieves higher learning accuracy, lowest training time. MSSA is effective even the space weather conditions are active during different solar phase periods.

Using Regularized Softmax Regression in the GNSS/INS Integrated Navigation System with Nonholonomic Constraints

The integration of global navigation satellite system (GNSS) and Inertial navigation system (INS) is widely implemented in land-vehicle navigation applications. However, the satellite signal is vulnerable in some special urban scenarios, consequently errors in terms of position, velocity and attitude grow rapidly in stand-alone mode especially for low-cost MEMS-based INS. In the conventional tight combination navigation schemes, system works on predicting model during the GNSS signal outage and the positioning accuracy is determined by the precision of the inertial navigation. Besides the lack of observation makes the estimate of inertial navigation error with GNSS information less reliable due to the satellite signal loss. In this paper, an improved non-holonomic constraints (NHC) method based on regularized softmax regression is proposed to enhance navigation precision when the number of visible satellite is insufficient. The velocity constraint condition is applied to simplify the system calculating equations of MEMS-based INS. Furthermore, a regularization softmax regression model based on the collected data is trained to recognize the vehicle motion pattern so as to realize deeper constraints. Simulation and field-test results indicate that the method is beneficial to raise the precision of low-cost GNSS/INS integrated navigation receiver by efficiently reduce the navigation errors.

An Extended Kalman Filter and Back Propagation Neural Network Algorithm Positioning Method Based on Anti-lock Brake Sensor and Global Navigation Satellite System Information.

Telematics box (T-Box) chip-level Global Navigation Satellite System (GNSS) receiver modules usually suffer from GNSS information failure or noise in urban environments. In order to resolve this issue, this paper presents a real-time positioning method for Extended Kalman Filter (EKF) and Back Propagation Neural Network (BPNN) algorithms based on Antilock Brake System (ABS) sensor and GNSS information. Experiments were performed using an assembly in the vehicle with a T-Box. The T-Box firstly use automotive kinematical Pre-EKF to fuse the four wheel speed, yaw rate and steering wheel angle data from the ABS sensor to obtain a more accurate vehicle speed and heading angle velocity. In order to reduce the noise of the GNSS information, After-EKF fusion vehicle speed, heading angle velocity and GNSS data were used and low-noise positioning data were obtained. The heading angle speed error is extracted as target and part of low-noise positioning data were used as input for training a BPNN model. When the positioning is invalid, the well-trained BPNN corrected heading angle velocity output and vehicle speed add the synthesized relative displacement to the previous absolute position to realize a new position. With the data of high-precision real-time kinematic differential positioning equipment as the reference, the use of the dual EKF can reduce the noise range of GNSS information and concentrate good-positioning signals of the road within 5 m (i.e. the positioning status is valid). When the GNSS information was shielded (making the positioning status invalid), and the previous data was regarded as a training sample, it is found that the vehicle achieved 15 minutes position without GNSS information on the recycling line. The results indicated this new position method can reduce the vehicle positioning noise when GNSS information is valid and determine the position during long periods of invalid GNSS information.

Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas.

This paper presents a method of fusing the ego-motion of a robot or a land vehicle estimated from an upward-facing camera with Global Navigation Satellite System (GNSS) signals for navigation purposes in urban environments. A sky-pointing camera is mounted on the top of a car and synchronized with a GNSS receiver. The advantages of this configuration are two-fold: firstly, for the GNSS signals, the upward-facing camera will be used to classify the acquired images into sky and non-sky (also known as segmentation). A satellite falling into the non-sky areas (e.g., buildings, trees) will be rejected and not considered for the final position solution computation. Secondly, the sky-pointing camera (with a field of view of about 90 degrees) is helpful for urban area ego-motion estimation in the sense that it does not see most of the moving objects (e.g., pedestrians, cars) and thus is able to estimate the ego-motion with fewer outliers than is typical with a forward-facing camera. The GNSS and visual information systems are tightly-coupled in a Kalman filter for the final position solution. Experimental results demonstrate the ability of the system to provide satisfactory navigation solutions and better accuracy than the GNSS-only and the loosely-coupled GNSS/vision, 20 percent and 82 percent (in the worst case) respectively, in a deep urban canyon, even in conditions with fewer than four GNSS satellites.

Method to Improve the Positioning Accuracy of Vehicular Nodes Using IEEE 802.11p Protocol

Currently, vehicle incidence remains high, but the related research is mainly focused on single-vehicle collision warning, which is unable to notice the following multiple endangered vehicles in emergency. The main technical challenge of chain collision warning is accurate and seamless ranging or positioning of the neighboring multiple vehicles. Because of the non-line of sight (NLOS) environments, the traditional ultrasonic or laser method does not work. At the same time, the accuracy of global navigation satellite system (GNSS) is over 10 m, such as global positioning system (GPS) or BeiDou satellite positioning system (BDS), which is not efficient for the cooperative collision avoidance (CCA) system. Moreover, GNSS fails to operate in NLOS tunnels or downtown areas where blockage of satellite signals is frequent. Thus, in this paper, a seamless and high accuracy positioning method based-on three kind of multi-source information fusion is proposed, i.e., the high accuracy local positioning information is provided by IEEE 802.11p protocol, the dual-mode wide area differential positioning information provided by the fusion of BDS and GPS, the positioning information of dead reckoning provided by the fusion of on-board diagnostic (OBD) and micro-electro-mechanical system-inertial navigation system. In vehicle positioning system, the accurate time of arrival (TOA) estimation is very important, so in this paper, a two steps method for TOA estimation using the IEEE 802.11p short preamble is proposed. Simulations show that in both the international telecommunication union vehicular multipath channel and the additive white Gaussian noise channel, the proposed method provides better accuracy and is less time complex than some well-known methods. The estimated TOA fused with the GNSS and OBD information can be used for seamless and high accuracy positioning in the CCA system to avoid traffic accident.

Data assimilation of GNSS zenith total delays from a Nordic processing centre

Abstract. Atmospheric moisture-related information estimated from Global Navigation Satellite System (GNSS) ground-based receiver stations by the Nordic GNSS Analysis Centre (NGAA) have been used within a state-of-the-art kilometre-scale numerical weather prediction system. Different processing techniques have been implemented to derive the moisture-related GNSS information in the form of zenith total delays (ZTDs) and these are described and compared. In addition full-scale data assimilation and modelling experiments have been carried out to investigate the impact of utilizing moisture-related GNSS data from the NGAA processing centre on a numerical weather prediction (NWP) model initial state and on the ensuing forecast quality. The sensitivity of results to aspects of the data processing, station density, bias-correction and data assimilation have been investigated. Results show benefits to forecast quality when using GNSS ZTD as an additional observation type. The results also show a sensitivity to thinning distance applied for GNSS ZTD observations but not to modifications to the number of predictors used in the variational bias correction applied. In addition, it is demonstrated that the assimilation of GNSS ZTD can benefit from more general data assimilation enhancements and that there is an interaction of GNSS ZTD with other types of observations used in the data assimilation. Future plans include further investigation of optimal thinning distances and application of more advanced data assimilation techniques.

A New Method for Land Vehicle Gravimetry Using SINS/VEL.

The use of Global Navigation Satellite System (GNSS) data for land vehicle gravimetry tests is challenged by complicated environments. A new approach for land vehicle gravimetry using a Strapdown Inertial Navigation System and velometer-integrated navigation computation (SINS/VEL) without using GNSS information has been put forward. Aided by the velometer with continuous longitudinal velocity output instead of GNSS signals, a SGA-WZ02 strapdown gravimeter that used the SINS/VEL method was tested in 2015. Four repeated lines were measured along a south-north direction highway in Eastern Changsha to verify the new method’s feasibility and performance. The gravity disturbance results showed an internal accuracy in scalar gravimetry about 1.17 mGal and 1.91 mGal for external accuracy assessment, with a spatial resolution of 1.7 km. Comparing this new method with the traditional SINS/GNSS gravimetry approach, it appeared that the results using SINS/VEL showed comparable internal and external accuracy. Theoretical analysis and practical test results showed that the new method was feasible for gravity determination by land dynamic vehicle.

Energy Efficient Cooperative Positioning Algorithm for Global Navigation Satellite System

Cooperative Positioning System (CPS) algorithm are suitable for GPS-challenged environments became a need of the hour because of its wide spread applications such as mobile communication. In cooperative positioning, Global Navigation Satellite System (GNSS) users or receivers exchange their GNSS data and information with their neighbors about their position. For CPS to be functioning GNSS user has to undergo three different task. The various estimation algorithms used in cooperative positioning are least squares, kalman filter, sum-product algorithm over a wireless network (SPWAN). These projects mainly focus on sum – product algorithm over a wireless network which can be implemented in a fully distributed way. Since it relies on a probabilistic, Bayesian approach algorithm are modeled as probability distribution functions. Experimental results shows reduced Localization error and minimum distance between the node. The power consumption of the nodes are reduced and thereby improving the battery life and increasing the throughput. Hence the lifetime of the network is been extended.

Localization and Driving Behavior Classification with Smartphone Sensors in Direct Absence of Global Navigation Satellite Systems

Global navigation satellite systems have tremendous impact and potential in the development of intelligent transportation systems and mobility services and are expected to deliver significant benefits, including increased capacity, improved safety, and decreased pollution. However, there are situations in which there might not be direct location information about vehicles, for example, in tunnels and in indoor facilities such as parking garages and commercial vehicle depots. Various technologies can be used for vehicle localization in these cases, and other sensors that are currently available in most modern smartphones, such as accelerometers and gyroscopes, can be used to obtain information directly about the driving patterns of individual drivers. The objective of this research is to present a framework for vehicle localization and modeling of driving behavior in indoor facilities or, more generally, facilities in which global navigation satellite system information is not available. Localization technologies and needs are surveyed and the adopted methodology is described. The case studies, which use data from multiple types of sensors (including accelerometers and gyroscopes from two smartphone platforms as well as two reference platforms), provide evidence that the opportunistic smart-phone sensors can be useful in identifying obstacles (e.g., speed humps) and maneuvers (e.g., U-turns and sharp turns). These data, when cross-referenced with a digital map of the facility, can be useful in positioning the vehicles in indoor environments. At a more macroscopic level, a methodology is presented and applied to determine the optimal number of clusters for the drivers’ behavior with a mix of suitable indexes.

GNSS-based operational monitoring devices for forest logging operation chains

The first results of a new approach for implementing operational monitoring tool to control the performance of forest mechanisation chains are proposed and discussed. The solution is based on Global Navigation Satellite System (GNSS) tools that are the core of a datalogging system that, in combination with a specific inference-engine, is able to analyse process times, work distances, forward speeds, vehicle tracking and number of working cycles in forest operations. As a consequence the operational monitoring control methods could provide an evaluation of the efficiency of the investigated forest operations. The study has monitored the performance of a tower yarder with crane and processor-head, during logging operations. The field surveys consisted on the installation of the GNSS device directly on the forest equipment for monitoring its movements. Simultaneously the field survey considered the integration of the GNSS information with a time study of work elements based on the continuous time methods supported by a time study board. Additionally, where possible, the onboard computer of the forest machine was also used in order to obtain additional information to be integrated to the GNSS data and the time study. All the recorded GNSS data integrated with the work elements study were thus post-processed through GIS analysis. The preliminary overview about the application of this approach on harvesting operations has permitted to assess a good feasibility of the use of GNSS in the relief of operative times in high mechanised forest chains. Results showed an easy and complete identification of the different operative cycles and elementary operations phases, with a maximum difference between the two methodologies of 10.32%. The use of GNSS installed on forest equipment, integrated with the inferenceengine and also with an interface for data communication or data storage, will permit an automatic or semi-automatic operational monitoring, improving the quantity of data and reducing the engagement of the surveyor.