Mapping Problem Research Articles

Pelatihan Penyelenggaraan Jenazah di Desa Bilanrengi Kecamatan Parigi Kabupaten Gowa

The Community Service Program (KKN) Batch V of STIBA Makassar students aims to increase the Islamic knowledge and religious skills in the people of Bilanrengi Village, Parigi District, Gowa Regency, especially in organizing of corpse according to Islamic law. Implementation of activities begins with mapping problems and needs using SOAR analysis. After analyzing the problems and needs, work programs are then prepared according to the needs of the community. The work program that was successfully implemented was the Training of Corpse Organizing. The method used is in the form of lectures, discussions, and practice, with the stages of PkM activities, namely: the observation, preparation, training, mentoring, and evaluation. The result of this activity was the amount of enthusiasm and support from the community for the training activities, especially elements from the village administration, village priests, taklim assemblies, and the community, especially ladies and gentlemen. The community feels great benefits after participating in this KKN program, including an increase in knowledge, insight and religious skills, especially related to organizing the human corpse in accordance with Islamic teachings. The continuation of this program is in the form of further coaching for participants, especially training on handling abnormal corpses, such as corpses with certain diseases, accident victims, victims of natural disasters, and others, which require special and different treatment from corpses in general.

Efficient Approach for Extracting High-Level B-Spline Features from LIDAR Data for Light-Weight Mapping

Light-weight and accurate mapping is made possible by high-level feature extraction from sensor readings. In this paper, the high-level B-spline features from a 2D LIDAR are extracted with a faster method as a solution to the mapping problem, making it possible for the robot to interact with its environment while navigating. The computation time of feature extraction is very crucial when mobile robots perform real-time tasks. In addition to the existing assessment measures of B-spline feature extraction methods, the paper also includes a new benchmark time metric for evaluating how well the extracted features perform. For point-to-point association, the most reliable vertex control points of the spline features generated from the hints of low-level point feature FALKO were chosen. The standard three indoor and one outdoor data sets were used for the experiment. The experimental results based on benchmark performance metrics, specifically computation time, show that the presented approach achieves better results than the state-of-the-art methods for extracting B-spline features. The classification of the methods implemented in the B-spline features detection and the algorithms are also presented in the paper.

Fusion of binocular vision, 2D lidar and IMU for outdoor localization and indoor planar mapping

Emergent fields such as Internet of Things applications, driverless cars, and indoor mobile robots have brought about an increasing demand for simultaneous localization and mapping (SLAM) technology. In this study, we design a SLAM scheme called BVLI-SLAM based on binocular vision, 2D lidar, and an inertial measurement unit (IMU) sensor. The pose estimation provided by vision and the IMU can provide better initial values for the 2D lidar mapping algorithm and improve the mapping effect. Lidar can also assist vision to provide better plane and yaw angle constraints in weak texture areas and obtain higher precision 6-degree of freedom pose. BVLI-SLAM uses graph optimization to fuse the data of the IMU, binocular camera, and laser. The IMU pre-integration combines the visual reprojection error and the laser matching error to form an error equation, which is processed by a sliding window-based bundle adjustment optimization to calculate the pose in real time. Outdoor experiments based on KITTI datasets and indoor experiments based on the trolley mobile measurement platform show that BVLI-SLAM has different degrees of improvement in mapping effect, positioning accuracy, and robustness compared with VINS-Fusion and Cartographer, and can solve the problem of positioning and plane mapping in indoor complex scenes.

An Improvement of a Mapping Method Based on Ant Colony Algorithm Applied to Smart Cities

The ant colony algorithm has been widely used in the field of data analysis of smart cities. However, the research of the traditional ant colony algorithm is more focused on one-to-one scenarios and there is insufficient research on many-to-one scenarios. Therefore, for the many-to-one topology mapping problem, this paper proposes a mapping method based on the ant colony algorithm. The design purpose of the mapping algorithm is to study the optimal mapping scheme, which can effectively reduce the cost of solving the problem. The core of the mapping algorithm is to design the objective function of the algorithm optimization. The commonly used optimization objective function and evaluation index is the average hop count; the average hop count is the most important indicator to measure the entire system. The smaller the average hop count, the less the pulse data needs to be forwarded, which can reduce the communication pressure of the system, reduce congestion, reduce the energy consumption caused by communication, and reduce the delay from the generation of pulse data to the response, etc. Therefore, this paper chooses the average hop count as the optimization objective and reduces the average hop count by designing a mapping algorithm. Through the simulation and verification of the improved ant colony algorithm in the scenario of many-to-one topology mapping, it is concluded that the final convergence result and convergence speed of the improved ant colony algorithm are significantly better than those of the traditional ant colony algorithm.

Adaptive chaotic map-based key extraction for efficient cross-layer authentication in VANETs

Vehicle-to-everything (V2X) communication is expected to offer users available and ultra-reliable transmission, particularly for critical applications related to safety and autonomy. In this context, establishing a secure and resilient authentication process with low latency and high functionality may not be achieved using conventional cryptographic methodologies due to their significant computation costs. Recent research has focused on employing the physical (PHY) characteristics of wireless channels to develop efficient discrimination techniques to overcome the shortcomings of crypto-based authentication. This paper presents a cross-layer authentication scheme for multicarrier communication, leveraging the spatially/temporally correlated wireless channel features to facilitate key verification without exposing its secrecy. By mapping the time-stamped hashed key and masking it with channel phase responses, we create a PHY-layer signature, allowing for verifying the sender's identity while employing the correlated channel responses between subcarriers to verify messages' integrity. Furthermore, we developed a Diffie-Hellman secret key extraction algorithm that employs the computationally intractable problems of the Chebyshev chaotic mapping for channel probing. Thus, terminals can extract high entropy shared keys that can be used to create dynamic PHY-layer signatures, supporting forward and backward secrecy. We evaluated the scheme's security strength against active/passive attacks. Besides theoretical analysis, we designed a 3-Dimensional (3D) scattering Doppler emulator to investigate the scheme's performance at different speeds of a moving vehicle and signal-to-noise ratios (SNRs) for a realistic vehicular channel. Theoretical and hardware implementation analyses proved the capability of the proposed scheme to support high detection probability at SNR ≥ 0 dB and speed ≤ 45 m/s.

A Multidimensional Adaptive Entropy Cloud-Model-Based Evaluation Method for Grid-Related Actions

Smart grid training system needs to evaluate actions during power grid operations in order to complete training for relevant personnel. The commonly used action evaluation methods are difficult for evaluating fine actions during power grid operations, and the evaluation results are subjective. The use of an effective method to evaluate the actions of the power grid operation is important for improving the smart grid training system, enhancing the skills of the trainers, and ensuring the personal safety of operators. This paper proposes a cloud attention mechanism and an evaluation method of grid-related actions based on a multidimensional adaptive entropy cloud model to complete the evaluation of fine actions in the grid’s operation process. Firstly, the OpenCV technique is used to obtain the data related to hand actions during grid operation and to extract the action features to complete the construction of multiscale date sets; then, the adaptive entropy weight matrix at different scales is constructed based on multiscale data sets using the cloud attention mechanism, and the basic cloud model is generated from original hand-action feature data; finally, the multidimensional adaptive entropy cloud model is constructed by the adaptive entropy weight matrix and the basic cloud model, and the multidimensional adaptive entropy cloud model obtained is compared with the multidimensional adaptive entropy cloud model generated based on the standard action features in the same space to obtain the evaluation level of the hand action. The results show that the evaluation method of grid-related actions based on the multidimensional adaptive entropy cloud model can solve the mutual mapping problem between quantitative indicators and qualitative evaluation results in the evaluation of grid operation processes relatively well, and it effectively solves the subjectivity of the weight assignment of evaluation indicators, which can be used for the evaluation of fine actions in the grid’s operation processes.

Application of multi-objective optimization in the study of anti-breast cancer candidate drugs

In the development of anti-breast cancer drugs, the quantitative structure-activity relationship model of compounds is usually used to select potential active compounds. However, the existing methods often have problems such as low model prediction performance, lack of overall consideration of the biological activity and related properties of compounds, and difficulty in directly selection candidate drugs. Therefore, this paper constructs a complete set of compound selection framework from three aspects: feature selection, relationship mapping and multi-objective optimization problem solving. In feature selection part, a feature selection method based on unsupervised spectral clustering is proposed. The selected features have more comprehensive information expression ability. In the relationship mapping part, a variety of machine learning algorithms are used for comparative experiments. Finally, the CatBoost algorithm is selected to perform the relationship mapping between each other, and better prediction performance is achieved. In the multi-objective optimization part, based on the analysis of the conflict relationship between the objectives, the AGE-MOEA algorithm is improved and used to solve this problem. Compared with various algorithms, the improved algorithm has better search performance.

TNN: A transfer learning classifier based on weighted nearest neighbors

Weighted nearest neighbors (WNN) classifiers are popular non-parametric classifiers. Despite the significant progress in WNN, most existing WNN classifiers are designed for traditional supervised learning problems where both training samples and test samples are assumed to be independent and identically distributed. However, in many real applications, it could be difficult or expensive to obtain training samples from the distribution of interest. Therefore, data collected from some related distributions are often used as supplementary training data for the classification task under the distribution of interest. It is essential to develop effective classification methods that could incorporate both training samples from the distribution of interest (if they exist) and supplementary training samples from a different but related distribution. To address this challenge, we propose a novel Transfer learning weighted Nearest Neighbors (TNN) classifier. As a WNN classifier, TNN determines the weights on the class labels of training samples for different test samples adaptively by minimizing an upper bound on the conditional expectation of the estimation error of the regression function. It puts decreasing weights on the class labels of the successive more distant neighbors. To accommodate the difference between training samples from the distribution of interest and supplementary training samples, TNN adds a non-negative offset to the distance between each supplementary training sample and the test sample, and thus constrains the excessive influence of the supplementary training samples on the prediction. Our theoretical studies show that, under certain conditions, TNN is consistent and minimax optimal (up to a logarithmic factor) in the covariate shift setting. In the posterior drift or the more general setting where both covariate shift and posterior drift exist, the excess risk of TNN depends on the maximum posterior discrepancy between the distribution of the supplementary training samples and the distribution of interest. Both our simulation studies and an application to the land use/land cover mapping problem in geography demonstrate that TNN outperforms other existing methods. It can serve as an effective tool for transfer learning.

Harmonic maps from surfaces of arbitrary genus into spheres

We relate the existence problem of harmonic maps into S^2 to the convex geometry of S^2. On one hand, this allows us to construct new examples of harmonic maps of degree 0 from compact surfaces of arbitrary genus into S^2. On the other hand, we produce new examples of regions that do not contain closed geodesics (that is, harmonic maps from S^1) but do contain images of harmonic maps from other domains. These regions can therefore not support a strictly convex functions. Our construction uses M. Struwe’s heat flow approach for the existence of harmonic maps from surfaces.

The Revisiting Problem in Simultaneous Localization and Mapping: A Survey on Visual Loop Closure Detection

Where am I? This is one of the most critical questions that any intelligent system should answer to decide whether it navigates to a previously visited area. This problem has long been acknowledged for its challenging nature in simultaneous localization and mapping (SLAM), wherein the robot needs to correctly associate the incoming sensory data to the database allowing consistent map generation. The significant advances in computer vision achieved over the last 20 years, the increased computational power, and the growing demand for long-term exploration contributed to efficiently performing such a complex task with inexpensive perception sensors. In this article, visual loop closure detection, which formulates a solution based solely on appearance input data, is surveyed. We start by briefly introducing place recognition and SLAM concepts in robotics. Then, we describe a loop closure detection system's structure, covering an extensive collection of topics, including the feature extraction, the environment representation, the decision-making step, and the evaluation process. We conclude by discussing open and new research challenges, particularly concerning the robustness in dynamic environments, the computational complexity, and scalability in long-term operations. The article aims to serve as a tutorial and a position paper for newcomers to visual loop closure detection.

Efficient Distributed Particle Filter for Robust Range-Only SLAM

Compared with simultaneous localization and mapping (SLAM) problems based on Lidar or visual sensors, range-only SLAM (RO-SLAM) is lacking bearing information. It brings challenges to particle sampling and SLAM estimation. This article proposes an efficient distributed particle filter (EDPF) for RO-SLAM problems. To overcome the difficulties of sampling in a high-dimensional state space, EDPF is decomposed into a set of subparticle filters (sub-PFs) with low dimensionality. Each sub-PF corresponds with an observed beacon, which directly reduces the sampling complexity. A joint weight update method is proposed to exploit the correlation among sub-PFs. It reweighs the particles via an auxiliary distribution after each sub-PF’s local filtering and embodies a better proposal distribution for distributed filtering. In addition, a beacon diagnosis method is proposed, it can detect and reinitialize the wrong converged beacon position estimates, which further reduces the SLAM error accumulation problem. We consider a RO-SLAM system with an odometer and ultrawideband (UWB) to verify the proposed EDPF. Results show that EDPF outperforms many existing RO-SLAM methods, which obtains the best performance with the acceptable computational load.

Network-level prediction of set-shifting deterioration after lower-grade glioma resection.

The aim of this study was to predict set-shifting deterioration after resection of low-grade glioma. The authors retrospectively analyzed a bicentric series of 102 patients who underwent surgery for low-grade glioma. The difference between the completion times of the Trail Making Test parts B and A (TMT B-A) was evaluated preoperatively and 3-4 months after surgery. High dimensionality of the information related to the surgical cavity topography was reduced to a small set of predictors in four different ways: 1) overlap between surgical cavity and each of the 122 cortical parcels composing Yeo's 17-network parcellation of the brain; 2) Tractotron: disconnection by the cavity of the major white matter bundles; 3) overlap between the surgical cavity and each of Yeo's networks; and 4) disconets: signature of structural disconnection by the cavity of each of Yeo's networks. A random forest algorithm was implemented to predict the postoperative change in the TMT B-A z-score. The last two network-based approaches yielded significant accuracies in left-out subjects (area under the receiver operating characteristic curve [AUC] approximately equal to 0.8, p approximately equal to 0.001) and outperformed the two alternatives. In single tree hierarchical models, the degree of damage to Yeo corticocortical network 12 (CC 12) was a critical node: patients with damage to CC 12 higher than 7.5% (cortical overlap) or 7.2% (disconets) had much higher risk to deteriorate, establishing for the first time a causal link between damage to this network and impaired set-shifting. The authors' results give strong support to the idea that network-level approaches are a powerful way to address the lesion-symptom mapping problem, enabling machine learning-powered individual outcome predictions.

NanoMap: A GPU-Accelerated OpenVDB-Based Mapping and Simulation Package for Robotic Agents

Encoding sensor data into a map is a problem that must be undertaken by any robotic agent operating in unknown or uncertain environments, and real-time updates are crucial to safe planning and control. Most modern robotic sensors produce some form of depth data or point cloud information that is only useful to the agent after being processed into the appropriate data structure, oftentimes an occupancy map. However, as the quality of sensor technology improves, so does the magnitude of the input data, which can creates a problem when trying to construct occupancy maps in real-time. Populating such an occupancy map using these dense point clouds can quickly become an expensive process, and many robotic agents have limited onboard computational bandwidth and memory. This results in delayed map updates and reduced operational performance in dynamic environments where real-time information is crucial for safe operation. However, while many modern robotic agents are still relatively limited by the power of onboard central processing units (CPUs), many platforms are gaining access to onboard graphics processing units (GPUs), and these resources remain underutilised with respect to the problem of occupancy mapping. We propose a novel probabilistic mapping solution that leverages a combination of OpenVDB, NanoVDB, and Nvidia’s Compute Unified Device Architecture (CUDA) to encode dense point clouds into OpenVDB data structures, leveraging the parallel compute strength of GPUs to provide significant speed advantages and further free up resources for tasks that cannot as easily be performed in parallel. An evaluation of our solution is provided, with performance benchmarks provided for both a laptop and a low power single board computer with onboard GPU. Similar performance improvements should be accessible on any system with access to a CUDA-compatible GPU. Additionally, our library provides the means to simulate one or more sensors on an agent operating within a randomly generated 3D-grid environment and create a live map for the purposes of evaluating planning and control techniques and for training agents via deep reinforcement learning. We also provide interface packages for the Robotic Operating System (ROS1) and the Robotic Operating System 2 (ROS2), and a ROS2 visualisation (RVIZ2) plugin for the underlying OpenVDB data structure.

Kinematic/Dynamic SLAM for Autonomous Vehicles Using the Linear Parameter Varying Approach.

Most existing algorithms in mobile robotics consider a kinematic robot model for the the Simultaneous Localization and Mapping (SLAM) problem. However, in the case of autonomous vehicles, because of the increase in the mass and velocities, a kinematic model is not enough to characterize some physical effects as, e.g., the slip angle. For this reason, when applying SLAM to autonomous vehicles, the model used should be augmented considering both kinematic and dynamic behaviours. The inclusion of dynamic behaviour implies that nonlinearities of the vehicle model are most important. For this reason, classical observation techniques based on the the linearization of the system model around the operation point, such as the well known Extended Kalman Filter (EKF), should be improved. Consequently, new techniques of advanced control must be introduced to more efficiently treat the nonlinearities of the involved models. The Linear Parameter Varying (LPV) technique allows working with nonlinear models, making a pseudolinear representation, and establishing systematic methodologies to design state estimation schemes applying several specifications. In recent years, it has been proved in many applications that this advanced technique is very useful in real applications, and it has been already implemented in a wide variety of application fields. In this article, we present a SLAM-based localization system for an autonomous vehicle considering the dynamic behaviour using LPV techniques. Comparison results are provided to show how our proposal outperforms classical observation techniques based on model linearization.

Algorithm for Finite Family of Variational Inequality with Fixed Point of Two Non-expansive Mappings

In the recent work, a new hybrid technique for computing the common solution of fixed point of a finite family of two non-expansive mapping and variational inequality problem for inverse strongly monotone mapping in a real Hilbert space is provided. We also demonstrate the convergence of the hybrid approach using an example.

Standard Dual Quaternion Optimization and Its Applications in Hand-Eye Calibration and SLAM

Several common dual quaternion functions, such as the power function, the magnitude function, the 2-norm function, and the kth largest eigenvalue of a dual quaternion Hermitian matrix, are standard dual quaternion functions, i.e., the standard parts of their function values depend upon only the standard parts of their dual quaternion variables. Furthermore, the sum, product, minimum, maximum, and composite functions of two standard dual functions, the logarithm and the exponential of standard unit dual quaternion functions, are still standard dual quaternion functions. On the other hand, the dual quaternion optimization problem, where objective and constraint function values are dual numbers but variables are dual quaternions, naturally arises from applications. We show that to solve an equality constrained dual quaternion optimization (EQDQO) problem, we only need to solve two quaternion optimization problems. If the involved dual quaternion functions are all standard, the optimization problem is called a standard dual quaternion optimization problem, and some better results hold. Then, we show that the dual quaternion optimization problems arising from the hand-eye calibration problem and the simultaneous localization and mapping (SLAM) problem are equality constrained standard dual quaternion optimization problems.

Machine learning based prediction of space group for Ba(Ce0.8-xZrx)Y0.2O3 perovskite-type protonic conductors

Space groups of Ba(Ce0.8-xZrx)Y0.2O3 (0 ≤ x ≤ 0.8) perovskite-type protonic conductors of wide temperature range from 25 to 900 °C in dry and wet atmospheres are predicted using various machine learning (ML) models. For the prediction, the data including the chemical formulas of the crystal materials as well as space group obtained via high-temperature X-ray diffraction (HT-XRD) and high-temperature neutron diffraction (HT-ND) between 25 and 1000 °C were inputted. 4 different ML models have been constructed using a Random Forest (RF), a Multi-Layer Perceptron (MLP) and a XGBoost, with different data set using experimental data (907 sets of 41 compositions), Magpie descriptor, and additional Inorganic Crystal Structure Database (ICSD) literature data (110 sets of 18 compositions). In the classification problem of space group prediction and phase diagram mapping, accuracy = 0.940 and F1 score = 0.785 is achieved by RF model with coordination number (C.N.) = 9 of oxygen of A-site cation.

Implicit iterative algorithms of the split common fixed point problem for Bregman quasi-nonexpansive mapping in Banach spaces

In this paper, we study a modified implicit rule for finding a solution of split common fixed point problem of a Bregman quasi-nonexpansive mapping in Banach spaces. We propose a new iterative algorithm and prove the strong convergence theorem under appropriate conditions. As an applica- tion, the results are applied to solving the zero problem and the equilibrium problem.

Corrections to: “Accurate and Robust Object SLAM With 3D Quadric Landmark Reconstruction in Outdoors”

The research presented in (Tian et al. 2012) deals with the usage of quadric landmarks in the Simultaneous Localization And Mapping (SLAM) problem. Although there is a lot of research in this subject, the quadric landmark initialization in outdoors is seldom addressed. In their work, (Tian et al. 2012) provides a complete and thorough approach for quadric initialization. As such, we have decided to implement the paper, but unfortunately encountered some issues with the initialized quadric values. Using a controlled scenario with a known quadric and ground-truth bounding-box measurements, we were able to trace back the problem to three specific equations in the original paper. After re-developing the problematic equations, we managed to find the correct formulation. We have decided to share our findings with the research community in an effort to give the work done in (Tian et al. 2012) the spotlight, as well as to save our fellow researchers the troubles we encountered when trying to implement this work.

3D gravity inversion based on deep learning

Gravity inversion is a typical geophysical inversion method that obtains the underground density distribution by analyzing the gravity anomaly. Normally, it can be divided into geophysics-based and deep learning based inversion. The 3D geophysics-based inversion is a time- and memory-consuming method, so 3D inversion is not routinely implemented in practical data interpretation. Here, we propose a deep learning method to transfer the 3D inversion problem to a multiple layers 2D mapping problem by decomposing the 3D target into four 2D images, including the horizontal location, vertical center, thickness and density distribution. This method is denoted as “decomposition network”. By implementing synthetic experiments with regular and complex models, and comparing with the 3D U-Net inversion, the proposed network has proved can reconstruct underground targets with high accuracy and high efficiency.