Mobile Robot Localization Research Articles

Indoor Localization and Path Planning of Mobile Disinfection and Epidemic Prevention Robot

Introduction: In response to the challenge of autonomous localization and navigation for mobile disinfection robots in unknown environments during the COVID-19 pandemic based on the existing patent technology of mobile robots, a localization and navigation approach combining the Cartographer-Simultaneous Localization and Mapping (SLAM) algorithm, an improved A* algorithm, and the Dynamic Window Approach (DWA) has been proposed, enabling effective disinfection and prevention work. Method: First, a comprehensive analysis of two laser SLAM algorithm frameworks, Gmapping- SLAM and Cartographer-SLAM, was conducted, which determined the Cartographer algorithm as the optimal SLAM algorithm for the mobile disinfection robot due to its superior performance. Second, an improved A* algorithm was developed based on its principles and integrated with the DWA algorithm for optimal path planning. Result: Simulation and prototype experiments demonstrated that the proposed approach effectively solved the autonomous localization and navigation challenges faced by mobile disinfection robots in unknown environments, enabling successful disinfection and prevention tasks. Conclusion: The proposed approach has the potential to lead to new patent applications in this field.

“Reinforcement learning particle swarm optimization based trajectory planning of autonomous ground vehicle using 2D LiDAR point cloud”

The advent of autonomous mobile robots has spurred research into efficient trajectory planning methods, particularly in dynamic environments with varied obstacles. This study focuses on optimizing trajectory planning for an Autonomous Ground Vehicle (AGV) using a novel Reinforcement Learning Particle Swarm Optimization (RLPSO) algorithm. Real-time mobile robot localization and map generation are introduced through the utilization of the Hector-SLAM algorithm within the Robot Operating System (ROS) platform, resulting in the creation of a binary occupancy grid. The present research thoroughly investigates the performance of the RLPSO algorithm, juxtaposed against five established Particle Swarm Optimization (PSO) variants, within the context of four distinct physical environments. The experimental design is tailored to emulate real-world scenarios, encompassing a spectrum of challenges posed by static and dynamic obstacles. The AGV, equipped with LiDAR sensors, navigates through diverse environments characterized by obstacles of different geometries. The RLPSO algorithm dynamically adapts its strategies based on feedback, enabling adaptable trajectory planning while effectively avoiding obstacles. Numerical results obtained from extensive experimentation highlight the algorithm's efficacy. The navigational model's validation is achieved within a MATLAB 2D virtual environment, employing 2D Lidar mapping point data. Transitioning to physical experiments with an AGV, RLPSO continues to demonstrate superior performance, showcasing its potential for real-world applications in autonomous navigation. On average, RLPSO achieves a 10–15 % reduction in path distances and traversal time compared to the following best-performing PSO variant across diverse scenarios. The adaptive nature of RLPSO, informed by feedback from the environment, distinguishes it as a promising solution for autonomous navigation in dynamic settings, with implications for practical implementation in real-world scenarios.

Moving horizon estimation for localization of mobile robots with measurement outliers

This paper investigates the moving horizon estimation (MHE) problem of mobile robots with measurement outliers. To deal with measurement outliers, the Euclidean distance of measurement error is introduced to detect and remove abnormal data. Then, we use dimension expansion methods to preprocess the data of heterogeneous sensors, such as UWB and IMU. An MHE-based method is proposed that deals with the localization of mobile robots with measurement outliers in the presence of bounded noise. An MHE-based estimator is obtained by solving a regularized least-squares problem. We analyze the convergence of the estimation error system using the properties of norm inequalities, and an upper bound is derived for the estimation error system by using norm inequality. Finally, simulation and experiment examples are given to verify the effectiveness and applicability of the proposed approach.

VIS-SLAM: A Real-Time Dynamic SLAM Algorithm Based on the Fusion of Visual, Inertial, and Semantic Information

A deep learning-based Visual Inertial SLAM technique is proposed in this paper to ensure accurate autonomous localization of mobile robots in environments with dynamic objects. Addressing the limitations of real-time performance in deep learning algorithms and the poor robustness of pure visual geometry algorithms, this paper presents a deep learning-based Visual Inertial SLAM technique. Firstly, a non-blocking model is designed to extract semantic information from images. Then, a motion probability hierarchy model is proposed to obtain prior motion probabilities of feature points. For image frames without semantic information, a motion probability propagation model is designed to determine the prior motion probabilities of feature points. Furthermore, considering that the output of inertial measurements is unaffected by dynamic objects, this paper integrates inertial measurement information to improve the estimation accuracy of feature point motion probabilities. An adaptive threshold-based motion probability estimation method is proposed, and finally, the positioning accuracy is enhanced by eliminating feature points with excessively high motion probabilities. Experimental results demonstrate that the proposed algorithm achieves accurate localization in dynamic environments while maintaining real-time performance.

An Improved Genetic Method for Satellite Selection in Multi-Global Navigation Satellite System Positioning for Mobile Robots

An Improved Genetic Method for Satellite Selection in Multi-Global Navigation Satellite System Positioning for Mobile Robots

Global UWB System: A High-Accuracy Mobile Robot Localization System With Tightly Coupled Integration

Global UWB System: A High-Accuracy Mobile Robot Localization System With Tightly Coupled Integration

Comparisson between generalized geometric triangulation and odometry

The localization in mobile robotics is essential for carrying out autonomous tasks. For this reason, different algorithms have been developed to estimate the robot's pose, either relatively or absolutely. One of the best known is Wheel-based Odometry, which is easy to implement but the error tends to increase with respect to time producing an unreliable estimation. In contrast, absolute localization algorithms such as Generalized Geometric Triangulation (GGT) offer higher accuracy, although their implementation may require more advanced measurement systems, and pose estimation can be slow. This work compares these two algorithms and shows what happens with the pose estimation when used for period of time. The presented results were obtained in a real testing area equipped with a Turtlebot3 model burger robot.

An Improved Trilateral Localization Technique Fusing Extended Kalman Filter for Mobile Construction Robot

Semi-open and chaotic environments of building sites are considered primary challenges for the localization of mobile construction robots. To mitigate environmental limitations, an improved trilateral localization technique based on artificial landmarks fusing the extended Kalman filters (EKFs) is proposed in this paper. The reflective intensity of the onboard laser is employed to identify artificial landmarks arranged in the ongoing construction environment. A trilateral positioning algorithm is then adopted and evaluated based on artificial landmarks. Multi-sensor fusion, combined with the EKF, is included to improve the positioning accuracy and reliability of the robot in complex conditions. We constructed validation scenarios in the Gazebo simulation environment to verify the required localization functionality. Concurrently, we established simulated testing environments in real-world settings, where the practicality of the proposed technique was validated through the fitting of ideal and actual localization trajectories. The effectiveness of the proposed technique was corroborated through comparative experimental results.

A compact RTK‐GNSS device for high‐precision localization of outdoor mobile robots

AbstractAccurate location information is crucial for ensuring the reliability of service robots, such as field vehicles and agricultural robots. These practical robots often encounter fluctuating or inclined terrains in dynamic conditions during operation. Additionally, the performance of mobile robots depends on the physical size and weight of the localization system–physically heavy systems may degrade performance. In this paper, we present a compact, lightweight RTK‐GNSS device weighing under 40 g, which achieves centimeter‐level accuracy across different attitudes and dynamic situations. To enhance signal reception and positioning performance, we lessen noise levels in the power circuit and radio‐frequency signal conditioning using a low‐dropout regulator and low‐noise amplifier. Utilizing the configured device, we verified centimeter‐level accuracy through experiments of tracking an unmanned ground vehicle. The potential of the device is substantiated via three application scenarios—a localization of a moving human for a wearable device, a heading estimation of a mobile robot in stationary conditions, and practical field tests in various signal‐reception environments. Attributable to its compact dimensions and lightweight features, the proposed device can yield a low system strain while maintaining high accuracy and reliable performance, rendering it highly adaptable for various robotic applications.

A review on positioning techniques of mobile robots

In this article, we have reviewed the available positioning, localization and navigation techniques for mobile robots. Different localization techniques based on diverse technologies are compared with one another, along with diverse algorithms and techniques for analyzing this information. The article highlights algorithms based on odometry, triangulation, visual analysis, and marker detection. The analysis included global, local, and personal location. One acquires knowledge on which method is suitable for indoor use and which for outdoor use, as well as the appropriate environmental conditions for each. The accuracy of the individual methods was compared with that of integrated systems consisting of several methods. For practical knowledge, it is possible to determine whether a particular method is cost-effective for a particular solution and to compare the expenses involved.

Real-time visual SLAM based YOLO-Fastest for dynamic scenes

Within the realm of autonomous robotic navigation, simultaneous localization and mapping (SLAM) serves as a critical perception technology, drawing heightened attention in contemporary research. The traditional SLAM systems perform well in static environments, but in the real physical world, dynamic objects can destroy the static geometric constraints of the SLAM system, further limiting its practical application in the real world. In this paper, a robust dynamic RGB-D SLAM system is proposed to expand the number of static points in the scene by combining with YOLO-Fastest to ensure the effectiveness of the geometric constraints model construction, and then based on that, a new thresholding model is designed to differentiate the dynamic features in the objection bounding box, which takes advantage of the double polyline constraints and the residuals after reprojection to filter the dynamic feature points. In addition, two Gaussian models are constructed to segment the moving objects in the bounding box in the depth image to achieve the effect similar to the instance segmentation under the premise of ensuring the computational speed. In this paper, experiments are conducted on dynamic sequences provided by the TUM dataset to evaluate the performance of the proposed method, and the results show that the root mean squared error metric of the absolute trajectory error of the algorithm of this paper has at least 80% improvement compared to ORB-SLAM2. Higher robustness in dynamic environments with both high and low dynamic sequences compared to DS-SLAM and Dynaslam, and can effectively provide intelligent localization and navigation for mobile robots.

基于多传感信息融合的移动机器人定位与环境感知技术

基于多传感信息融合的移动机器人定位与环境感知技术

How Safe Is Particle Filtering-Based Localization for Mobile Robots? An Integrity Monitoring Approach

How Safe Is Particle Filtering-Based Localization for Mobile Robots? An Integrity Monitoring Approach

Indoor Localization of Mobile Robots Based on the Fusion of an Improved AMCL Algorithm and a Collision Algorithm

Indoor Localization of Mobile Robots Based on the Fusion of an Improved AMCL Algorithm and a Collision Algorithm

Laser Location of Mobile Robot Based on ICP Algorithm

Laser Location of Mobile Robot Based on ICP Algorithm

Quantifying the Risk of Unmapped Associations for Mobile Robot Localization Safety

Quantifying the Risk of Unmapped Associations for Mobile Robot Localization Safety

Improving odometric sensor performance by real-time error processing and variable covariance

This paper presents a new method to increase odometric sensor accuracy by systematic and non-systematic errors processing. Mobile robot localization is improved combining this technique with a filter that fuses the information from several sensors characterized by their covariance. The process focuses on calculating the odometric speed difference with respect to the filter to implement an error type detection module in real time. The correction of systematic errors consists in an online parameter adjustment using the previous information and conditioned by the filter accuracy. This data is also applied to design a variable odometric covariance which describes the sensor reliability and determines the influence of both errors on the robot localization. The method is implemented in a low-cost autonomous wheelchair with a LIDAR, IMU and encoders fused by an UKF algorithm. The experimental results prove that the estimated poses are closer to the real ones than using other well-known previous methods.

Multi-Sensor Fusion Simultaneous Localization Mapping Based on Deep Reinforcement Learning and Multi-Model Adaptive Estimation.

In this study, we designed a multi-sensor fusion technique based on deep reinforcement learning (DRL) mechanisms and multi-model adaptive estimation (MMAE) for simultaneous localization and mapping (SLAM). The LiDAR-based point-to-line iterative closest point (PLICP) and RGB-D camera-based ORBSLAM2 methods were utilized to estimate the localization of mobile robots. The residual value anomaly detection was combined with the Proximal Policy Optimization (PPO)-based DRL model to accomplish the optimal adjustment of weights among different localization algorithms. Two kinds of indoor simulation environments were established by using the Gazebo simulator to validate the multi-model adaptive estimation localization performance, which is used in this paper. The experimental results of the proposed method in this study confirmed that it can effectively fuse the localization information from multiple sensors and enable mobile robots to obtain higher localization accuracy than the traditional PLICP and ORBSLAM2. It was also found that the proposed method increases the localization stability of mobile robots in complex environments.

Visual sensor aided by image processing based on optimization algorithm research of mobile robot localization

Visual sensor aided by image processing based on optimization algorithm research of mobile robot localization

Multisensory integration for topological indoor localization of mobile robots in complex symmetrical environments

Indoor localization is essential for robotic navigation by using different sensors on board. Specifically, visual localization with a single camera is a great challenge in highly symmetric environments (e.g. offices, hospitals or residences), where appearance patterns are repetitive and captures from different locations provide very similar images. To overcome this issue, in this paper, we present a method that integrates multisensory information from an RGB-D camera, a LiDAR and motor encoders. Our approach simultaneously utilizes spatial consistency from a reference topological map and temporal consistency from time-series observations. Inspired by human cognitive perception, we define a two layered topological architecture that encompasses both coarse information of object distributions and structural information with some metric references. Categories of common objects in the environments, such as fire extinguishers or doors, are used as natural beacons. We evaluated our approach in two real-world buildings based on a multi-aisle structure with corridors of very similar appearance. Results demonstrated accurate localization despite the high level of symmetry of the scenario, and how ambiguity was significantly reduced as the agent progressed along its trajectory.