Review of Simultaneous Localization and Mapping Technology in the Agricultural Environment

Simultaneous localization and mapping(SLAM)is one of the most attractive research hotspots in the field of robotics,and it is also a prerequisite for the autonomous navigation of robots.It can significantly improve the autonomous navigation ability of mobile robots and their adaptability to different application environments and contribute to the realization of real-time obstacle avoidance and dynamic path planning.Moreover,the application of SLAM technology has expanded from industrial production,intelligent transportation,special operations and other fields to agricultural environments,such as autonomous navigation,independent weeding,three-dimen-sional(3D)mapping,and independent harvesting.This paper mainly introduces the principle,sys-tem framework,latest development and application of SLAM technology,especially in agricultural environments.Firstly,the system framework and theory of the SLAM algorithm are introduced,and the SLAM algorithm is described in detail according to different sensor types.Then,the devel-opment and application of SLAM in the agricultural environment are summarized from two aspects:environment map construction,and localization and navigation of agricultural robots.Finally,the challenges and future research directions of SLAM in the agricultural environment are discussed.

Analyzing the Impact of Scene Transitions on Indoor Camera Localization through Scene Change Detection in Real-Time

Real-time indoor camera localization is a significant problem in indoor robot navigation and surveillance systems.The scene can change during the image sequence and plays a vital role in the localization performance of robotic applications in terms of accuracy and speed.This research proposed a real-time indoor camera localization system based on a recurrent neural network that detects scene change during the image sequence.An annotated image dataset trains the proposed system and predicts the camera pose in real-time.The system mainly improved the localization performance of indoor cameras by more accurately predicting the camera pose.It also recognizes the scene changes during the sequence and evaluates the effects of these changes.This system achieved high accuracy and real-time performance.The scene change detection process was performed using visual rhythm and the proposed recurrent deep architecture,which performed camera pose prediction and scene change impact evaluation.Overall,this study proposed a novel real-time localization system for indoor cameras that detects scene changes and shows how they affect localization performance.

Approach of simultaneous localization and mapping based on local maps for robot

An extended Kalman filter approach of simultaneous localization and mapping(SLAM) was proposed based on local maps. A local frame of reference was established periodically at the position of the robot, and then the observations of the robot and landmarks were fused into the global frame of reference. Because of the independence of the local map, the approach does not cumulate the estimate and calculation errors which are produced by SLAM using Kalman filter directly. At the same time, it reduces the computational complexity. This method is proven correct and feasible in simulation experiments.

Robust Iterated Sigma Point FastSLAM Algorithm for Mobile Robot Simultaneous Localization and Mapping

Simultaneous localization and mapping （SLAM） is a key technology for mobile robots operating under unknown environment. While FastSLAM algorithm is a popular solution to the SLAM problem, it suffers from two major drawbacks： one is particle set degeneracy due to lack of observation information in proposal distribution design of the particle filter; the other is errors accumulation caused by linearization of the nonlinear robot motion model and the nonlinear environment observation model. For the purpose of overcoming the above problems, a new iterated sigma point FastSLAM （ISP-FastSLAM） algorithm is proposed. The main contribution of the algorithm lies in the utilization of iterated sigma point Kalman filter （ISPKF）, which minimizes statistical linearization error through Gaussian-Newton iteration, to design an optimal proposal distribution of the particle filter and to estimate the environment landmarks. On the basis of Rao-Blackwellized particle filter, the proposed ISP-FastSLAM algorithm is comprised by two main parts： in the first part, an iterated sigma point particle filter （ISPPF） to localize the robot is proposed, in which the proposal distribution is accurately estimated by the ISPKF; in the second part, a set of ISPKFs is used to estimate the environment landmarks. The simulation test of the proposed ISP-FastSLAM algorithm compared with FastSLAM2.0 algorithm and Unscented FastSLAM algorithm is carried out, and the performances of the three algorithms are compared. The simulation and comparing results show that the proposed ISP-FastSLAM outperforms other two algorithms both in accuracy and in robustness. The proposed algorithm provides reference for the optimization research of FastSLAM algorithm.

Research on simultaneous localization and mapping for AUV by an improved method:Variance reduction FastSLAM with simulated annealing

At present,simultaneous localization and mapping(SLAM) for an autonomous underwater vehicle(AUV)is a research hotspot.Aiming at the problem of non-linear model and non-Gaussian noise in AUV motion,an improved method of variance reduction fast simultaneous localization and mapping(FastSLAM) with simulated annealing is proposed to solve the problems of particle degradation,particle depletion and particle loss in traditional FastSLAM,which lead to the reduction of AUV location estimation accuracy.The adaptive exponential fading factor is generated by the anneal function of simulated annealing algorithm to improve the effective particle number and replace resampling.By increasing the weight of small particles and decreasing the weight of large particles,the variance of particle weight can be reduced,the number of effective particles can be increased,and the accuracy of AUV location and feature location estimation can be improved to some extent by retaining more information carried by particles.The experimental results based on trial data show that the proposed simulated annealing variance reduction FastSLAM method avoids particle degradation,maintains the diversity of particles,weakened the degeneracy and improves the accuracy and stability of AUV navigation and localization system.

A novel method for mobile robot simultaneous localization and mapping

A novel mobile robot simultaneous localization and mapping (SLAM) method is implemented by using the Rao- Blackwellized particle filter (RBPF) for monocular vision-based autonomous robot in unknown indoor environment. The particle filter combined with unscented Kalman filter (UKF) for extending the path posterior by sampling new poses integrating the current observation. Landmark position estimation and update is implemented through UKF. Furthermore, the number of resampling steps is determined adaptively, which greatly reduces the particle depletion problem. Monocular CCD camera mounted on the robot tracks the 3D natural point landmarks structured with matching image feature pairs extracted through Scale Invariant Feature Transform (SIFT). The matching for multi-dimension SIFT features which are highly distinctive due to a special descriptor is implemented with a KD-Tree. Experiments on the robot Pioneer3 showed that our method is very precise and stable.

Immune evolutionary algorithms with domain knowledge for simultaneous localization and mapping

Immune evolutionary algorithms with domain knowledge were presented to solve the problem of simultaneous localization and mapping for a mobile robot in unknown environments. Two operators with domain knowledge were designed in algorithms, where the feature of parallel line segments without the problem of data association was used to construct a vaccination operator, and the characters of convex vertices in polygonal obstacle were extended to develop a pulling operator of key point grid. The experimental results of a real mobile robot show that the computational expensiveness of algorithms designed is less than other evolutionary algorithms for simultaneous localization and mapping and the maps obtained are very accurate. Because immune evolutionary algorithms with domain knowledge have some advantages, the convergence rate of designed algorithms is about 44% higher than those of other algorithms.

A survey: which features are required for dynamic visual simultaneous localization and mapping?

In recent years,simultaneous localization and mapping in dynamic environments(dynamic SLAM)has attracted significant attention from both academia and industry.Some pioneering work on this technique has expanded the potential of robotic applications.Compared to standard SLAM under the static world assumption,dynamic SLAM divides features into static and dynamic categories and leverages each type of feature properly.Therefore,dynamic SLAM can provide more robust localization for intelligent robots that operate in complex dynamic environments.Additionally,to meet the demands of some high-level tasks,dynamic SLAM can be integrated with multiple object tracking.This article presents a survey on dynamic SLAM from the perspective of feature choices.A discussion of the advantages and disadvantages of different visual features is provided in this article.

Mobile Robot Hierarchical Simultaneous Localization and Mapping Using Monocular Vision

A hierarchical mobile robot simultaneous localization and mapping (SLAM) method that allows us to obtain accurate maps was presented. The local map level is composed of a set of local metric feature maps that are guaranteed to be statistically independent. The global level is a topological graph whose arcs are labeled with the relative location between local maps. An estimation of these relative locations is maintained with local map alignment algorithm, and more accurate estimation is calculated through a global minimization procedure using the loop closure constraint. The local map is built with Rao-Blackwellised particle filter (RBPF), where the particle filter is used to extending the path posterior by sampling new poses. The landmark position estimation and update is implemented through extended Kalman filter (EKF). Monocular vision mounted on the robot tracks the 3D natural point landmarks, which are structured with matching scale invariant feature transform (SIFT) feature pairs. The matching for multi-dimension SIFT features is implemented with a KD-tree in the time cost of O(lbN). Experiment results on Pioneer mobile robot in a real indoor environment show the superior performance of our proposed method.

Rapid State Augmentation for Compressed EKF-Based Simultaneous Localization and Mapping

A new method for speeding up the state augment operations involved in the compressed extended Kalman filter-based simultaneous localization and mapping （CEKF-SLAM） algorithm was proposed. State augment usually requires a fully-updated state eovariance so as to append the information of newly observed landmarks, thus computational volume increases quadratically with the number of landmarks in the whole map. It was proved that state augment can also be achieved by augmenting just one auxiliary coefficient ma- trix. This method can yield identical estimation results as those using EKF-SLAM algorithm, and computa- tional amount grows only linearly with number of increased landmarks in the local map. The efficiency of this quick state augment for CEKF-SLAM algorithm has been validated by a sophisticated simulation project.

Simultaneous Localization and Mapping System Based on Labels

In this paper a label-based simultaneous localization and mapping（ SLAM） system is proposed to provide localization to indoor autonomous robots. In the system quick response（ QR） codes encoded with serial numbers are utilized as labels. These labels are captured by two webcams,then the distances and angles between the labels and webcams are computed. Motion estimated from the two rear wheel encoders is adjusted by observing QR codes. Our system uses the extended Kalman filter（ EKF） for the back-end state estimation. The number of deployed labels controls the state estimation dimension. The label-based EKF-SLAM system eliminates complicated processes,such as data association and loop closure detection in traditional feature-based visual SLAM systems. Our experiments include software-simulation and robot-platform test in a real environment. Results demonstrate that the system has the capability of correcting accumulated errors of dead reckoning and therefore has the advantage of superior precision.

Underwater Simultaneous Localization and Mapping Based on Forward-looking Sonar

A method of underwater simultaneous localization and mapping （SLAM） based on forward-looking sonar was proposed in this paper. Positions of objects were obtained by the forward-looking sonar, and an improved association method based on an ant colony algorithm was introduced to estimate the positions. In order to improve the precision of the positions, the extended Kalman filter （EKF） was adopted. The presented algorithm was tested in a tank, and the maximum estimation error of SLAM gained was 0.25 m. The tests verify that this method can maintain better association efficiency and reduce navigatioJ~ error.

Mobile robot simultaneous localization and map building based on improved particle filter

We present an investigation into the use of pan tilt zoom camera and sonar sensors for simuhaneous localization and mapping with artificial colored landmarks. An improved particle filter is applied to estimate a posterior of the pose of the robot, in which each particle has associated it with an entire map. The distributions of landmarks are also represented by particle sets, where separate particles are used to represent the robot and the landmarks. Hough transform is used to extract line segments from sonar observations and build map simultaneously. The key advantage of our method is that the full posterior over robot poses and landmarks can be nonlinearly approximated at every point in time by particles. Especially the landmarks are affixed on the moving robots, which can reduce the impact of the depletion problem and the impoverishment problem produced by basic particle filter. Experimental results show that this approach has advantages over the basic particle filter and the extended Kalman filter.

Arterial stimulation with simultaneous venous sampling for localizing insulinomas

Purpose: The present study aimed to assess the accuracies of arterial stimulation with simultaneous venous sampling(ASVS) in preoperative localization of insulinomas. Materials and Methods: A cohort consisting of 6 males and 14 females(median age, 48.5y; range, 28–62y) with pathologically proven insulinomas were included in this study. Selective angiographies were performed with the superior mesenteric artery(SMA), gastroduodenal artery(GDA), proximal splenic artery, and midsplenic artery in all individuals. Then ASVS procedures were followed after angiographies for these arteries. Clinical characteristics of the patient and the tumor number, location, and size were recorded. The accuracy of preoperative localization of insulinomas was tested. Results: A total of 22 tumors were identified by histopathological diagnosis. The mean size of the tumor was 1.40±0.60 cm. Five tumors were in the head/neck region and 17 in the body/tail region. ASVS accurately localized 17/20(85%) cases with only biochemical data and 19/20(95%) cases with biochemical data and angiography images. Variant pancreatic arterial anatomy was revealed in 2 false cases with inferior pancreatic artery replaced by the superior mesenteric artery. Conclusion: ASVS was highly accurate in localizing insulinomas and should be performed in most of the patients with suspected insulinomas before the operation.

THE SIMULTANEOUS APPROXIMATION IN THE LOCALLY CONVEX SPACES

In this paper, we study the characterization of f-Chebyshev radius and f-Chebyshev centers and the existence of f-Chebyshev centers in locally convex spaces.

Study on noise in simultaneous geomagnetic difference data caused by the effect of S_q local-time variation

A new concept is suggested on tectonomagnetic research about the noise in simultaneous geomagnetic difference data caused by the effect of Sq local-time variation, together with the method of theoretical calculation. The level of the noise and its contribution to the total noises of the differences data are analyzed. The result indicates that the noise increases linearly with the increase of the distance between the two stations in the range of 40° longitude-difference, and its increasing rate is about 0.4 nT/（°）at latitude 40°N. The example calculated at a pair of sites with longitude-difference 0.357°, shows that the noise is about one fifth of the total noises of the difference data on geomagnetic quiet-day.

Mobile robot localization algorithm based on multi-sensor information fusion

In order to effectively reduce the uncertainty error of mobile robot localization with a single sensor and improve the accuracy and robustness of robot localization and mapping,a mobile robot localization algorithm based on multi-sensor information fusion(MSIF)was proposed.In this paper,simultaneous localization and mapping(SLAM)was realized on the basis of laser Rao-Blackwellized particle filter(RBPF)-SLAM algorithm and graph-based optimization theory was used to constrain and optimize the pose estimation results of Monte Carlo localization.The feature point extraction and quadrilateral closed loop matching algorithm based on oriented FAST and rotated BRIEF(ORB)were improved aiming at the problems of generous calculation and low tracking accuracy in visual information processing by means of the three-dimensional(3D)point feature in binocular visual reconstruction environment.Factor graph model was used for the information fusion under the maximum posterior probability criterion for laser RBPF-SLAM localization and binocular visual localization.The results of simulation and experiment indicate that localization accuracy of the above-mentioned method is higher than that of traditional RBPF-SLAM algorithm and general improved algorithms,and the effectiveness and usefulness of the proposed method are verified.

Localization and mapping in urban area based on 3D point cloud of autonomous vehicles

In order to meet the application requirements of autonomous vehicles, this paper proposes a simultaneous localization and mapping （SLAM） algorithm, which uses a VoxelGrid filter to down sample the point cloud data, with the combination of iterative closest points （ICP） algorithm and Gaussian model for particles updating, the matching between the local map and the global map to quantify particles＇ importance weight. The crude estimation by using ICP algorithm can find the high probability area of autonomous vehicles＇ poses, which would decrease particle numbers, increase algorithm speed and restrain particles＇ impoverishment. The calculation of particles＇ importance weight based on matching of attribute between grid maps is simple and practicable. Experiments carried out with the autonomous vehicle platform validate the effectiveness of our approaches.

Robust SLAM localization method based on improved variational Bayesian filtering

Aimed at the problem that the state estimation in the measurement update of the simultaneous localization and mapping(SLAM)method is incorrect or even not convergent because of the non-Gaussian measurement noise,outliers,or unknown and time-varying noise statistical characteristics,a robust SLAM method based on the improved variational Bayesian adaptive Kalman filtering(IVBAKF)is proposed.First,the measurement noise covariance is estimated using the variable Bayesian adaptive filtering algorithm.Then,the estimated covariance matrix is robustly processed through the weight function constructed in the form of a reweighted average.Finally,the system updates are iterated multiple times to further gradually correct the state estimation error.Furthermore,to observe features at different depths,a feature measurement model containing depth parameters is constructed.Experimental results show that when the measurement noise does not obey the Gaussian distribution and there are outliers in the measurement information,compared with the variational Bayesian adaptive SLAM method,the positioning accuracy of the proposed method is improved by 17.23%,20.46%,and 17.76%,which has better applicability and robustness to environmental disturbance.

Computer Geometries for Finding All Real Zeros of Polynomial Equations Simultaneously

In this research article,we construct a family of derivative free simultaneous numerical schemes to approximate all real zero of non-linear polynomial equation.We make a comparative analysis of the newly constructed numerical schemes with a well-known existing simultaneous method for determining all the distinct real zeros of polynomial equations using computer algebra system Mat Lab.Lower bound of convergence of simultaneous schemes is calculated using Mathematica.Global convergence property of the numerical schemes is presented by taking random starting initial approximation and their convergence history are graphically presented.Some real life engineering applications along with some higher degree polynomials are considered as numerical test problems to show performance and efficiency of the derivative free family of numerical methods with comparison of an existing method of same order in literature.Local computational order of convergence,CPU time,graph of computational order of convergence and residual error graphs elaborate efficiency,robustness and authentication of the suggested family of numerical methods in its domain.