Adaptive Monte Carlo Localization Research Articles

Map Construction and Positioning Method for LiDAR SLAM-Based Navigation of an Agricultural Field Inspection Robot

In agricultural field inspection robots, constructing accurate environmental maps and achieving precise localization are essential for effective Light Detection And Ranging (LiDAR) Simultaneous Localization And Mapping (SLAM) navigation. However, navigating in occluded environments, such as mapping distortion and substantial cumulative errors, presents challenges. Although current filter-based algorithms and graph optimization-based algorithms are exceptionally outstanding, they exhibit a high degree of complexity. This paper aims to investigate precise mapping and localization methods for robots, facilitating accurate LiDAR SLAM navigation in agricultural environments characterized by occlusions. Initially, a LiDAR SLAM point cloud mapping scheme is proposed based on the LiDAR Odometry And Mapping (LOAM) framework, tailored to the operational requirements of the robot. Then, the GNU Image Manipulation Program (GIMP) is employed for map optimization. This approach simplifies the map optimization process for autonomous navigation systems and aids in converting the Costmap. Finally, the Adaptive Monte Carlo Localization (AMCL) method is implemented for the robot’s positioning, using sensor data from the robot. Experimental results highlight that during outdoor navigation tests, when the robot operates at a speed of 1.6 m/s, the average error between the mapped values and actual measurements is 0.205 m. The results demonstrate that our method effectively prevents navigation mapping distortion and facilitates reliable robot positioning in experimental settings.

Mapping and Localization of Autonomous Mobile Robots in Simulated Indoor Environments

Autonomously making a map, localizing within it, and planning with it are fundamental problems in mobile robotics. Every autonomous mobile robot system must include a solution to all three problems. These three problems are interconnected, with simultaneous localization and mapping (SLAM) being a well-known issue. However, there is indeed a growing and developing realization in the research field that path planning how a robot goes about mapping and finding an environment (and then operating in the environment such as starting to the destination point) can avoid degenerate conditions and greatly reduce SLAM complexity. In this paper, the implementation of an autonomous mobile robot system for indoor environments using open-source ROS packages and a combination of cartography algorithm and adaptive Monte Carlo localization (AMCL) algorithms has been implemented. The system addresses the challenge of developing three components such as mapping, localization, and path planning systems for indoor autonomous mobile robots. The mapping module creates a global map using the cartography ROS package and SLAM algorithm. The localization module estimates the robot's pose using the AMCL approach. The planning module generates collision-free trajectories and control commands using the moving base ROS package. The experimental results demonstrate the effectiveness of this approach and its valuable contribution to the robotics field. The cartography algorithm mapping algorithm generates accurate and reliable maps, while the localization algorithm successfully determines the robot's position with good performance. Additionally, the path planning algorithm effectively avoids both static and dynamic obstacles, ensuring smooth navigation in the environment.

Optimal path planning using bidirectional rapidly-exploring random tree star-dynamic window approach (BRRT*-DWA) with adaptive Monte Carlo localization (AMCL) for mobile robot

Path planning is an important task for mobileF service robots. Most of the available path-planning algorithms are applicable only in static environments. Achieving path planning becomes a difficult task in an unknown, dynamic environment. To solve the path planning problem in an unknown dynamic environment, this paper proposes a Bidirectional Rapidly-exploring Random Tree Star-Dynamic Window Approach (BRRT*-DWA) algorithm with Adaptive Monte Carlo Localization (AMCL). Bidirectional Rapidly-exploring Random Tree Star(BRRT*) is used to generate an optimal global path plan, Dynamic Window Approach(DWA) is a local planner and Adaptive Monte Carlo Localization(AMCL) is used as a localization technique. The robot can navigate using the map file of the unknown environment created by Simultaneous Localization And Mapping (SLAM) and the data from the Light Detection and Ranging (LiDAR) sensor while avoiding dynamic and static obstacles. In addition, the object identification algorithm You Only Look Once (YOLO) was adopted, trained, and used for the robot to recognize objects and people. Results obtained from both simulation and experiment show the proposed method can achieve better performance in a dynamic environment compared with other state-of-the-art algorithms.

Adaptive Monte Carlo Localization in Unstructured Environment via the Dimension Chain of Semantic Corners

Adaptive Monte Carlo Localization in Unstructured Environment via the Dimension Chain of Semantic Corners

Path Planning for Autonomous Mobile Robot Using Intelligent Algorithms

Machine learning technologies are being integrated into robotic systems faster to enhance their efficacy and adaptability in dynamic environments. The primary goal of this research was to propose a method to develop an Autonomous Mobile Robot (AMR) that integrates Simultaneous Localization and Mapping (SLAM), odometry, and artificial vision based on deep learning (DL). All are executed on a high-performance Jetson Nano embedded system, specifically emphasizing SLAM-based obstacle avoidance and path planning using the Adaptive Monte Carlo Localization (AMCL) algorithm. Two Convolutional Neural Networks (CNNs) were selected due to their proven effectiveness in image and pattern recognition tasks. The ResNet18 and YOLOv3 algorithms facilitate scene perception, enabling the robot to interpret its environment effectively. Both algorithms were implemented for real-time object detection, identifying and classifying objects within the robot’s environment. These algorithms were selected to evaluate their performance metrics, which are critical for real-time applications. A comparative analysis of the proposed DL models focused on enhancing vision systems for autonomous mobile robots. Several simulations and real-world trials were conducted to evaluate the performance and adaptability of these models in navigating complex environments. The proposed vision system with CNN ResNet18 achieved an average accuracy of 98.5%, a precision of 96.91%, a recall of 97%, and an F1-score of 98.5%. However, the YOLOv3 model achieved an average accuracy of 96%, a precision of 96.2%, a recall of 96%, and an F1-score of 95.99%. These results underscore the effectiveness of the proposed intelligent algorithms, robust embedded hardware, and sensors in robotic applications. This study proves that advanced DL algorithms work well in robots and could be used in many fields, such as transportation and assembly. As a consequence of the findings, intelligent systems could be implemented more widely in the operation and development of AMRs.

Autonomous navigation system for greenhouse tomato picking robots based on laser SLAM

Aiming at the specific operation path cruising problem of greenhouse tomato picking robots, this paper designs a laser SLAM (Simultaneous Localization And Mapping)-based autonomous navigation system for greenhouse tomato picking robots.The autonomous navigation system utilizes LiDAR to detect the greenhouse environment, creates an environment map with the Gmapping algorithm employing particle filtering, locates the robot with the AMCL (Adaptive Monte Carlo Localization) algorithm, devises the robot's path for picking tasks with the A* algorithm, and dynamically navigates around obstacles during operations with the DWA (Dynamic Window Approach) algorithm. The greenhouse experiment revealed that the robot's average deviance for both directions (X and Y) is 2.87 and 0.80 centimeters, respectively, when traveling a 320 centimeter path. On the other hand, its average variation across the directions of X and Y is 1.48 versus 1.08 centimeters when it passes through a 40 centimeter zone. The robot exhibits averaged variations of 1.73 and 2.24 millimeters in the X and Y axes, each, when navigating a 40-centimeter-radius turn. Furthermore, the robot exhibits average variations of 1.12 centimeters in the X and 0.78 centimeters in the Y directions when it follows a certain "S"-shaped course. Based on the fulfillment of prescribed path cruising, the system has a high level of navigation and positioning accuracy, better meeting the requirements for greenhouse tomato harvesting robot direction and placement.

Multi-sensor fusion based wheeled robot research on indoor positioning method

In response to the poor positioning performance and errors of the wheeled robot under a single sensor, a combination of wheel odometry dead reckoning, Inertial Measurement Unit (IMU) heading angle information, and map environment information obtained from LiDAR is used for indoor positioning of the wheeled robot. By using data from multiple sensors, errors are reduced and positioning accuracy is improved. The results show that combining the Extended Kalman Filter (EKF), Gmapping algorithm, and Adaptive Monte Carlo Localization (AMCL) reduces the relative error by 8.35 % compared to the single wheel odometry heading calculation method. Compared with the EKF fusion method for dead reckoning and inertial measurement unit heading angle information, the relative error is reduced by 4 %, and there is no cumulative trend in angle error. This effectively addresses the challenge of cumulative errors in indoor positioning within environments that lack base stations, thereby enhancing accuracy and reliability in such specialized conditions.

L-PCM: Localization and Point Cloud Registration-Based Method for Pose Calibration of Mobile Robots

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

Proposal for Navigation System Using Three-Dimensional Maps—Self-Localization Using a 3D Map and Slope Detection Using a 2D Laser Range Finder and 3D Map

Many teams participating in robotic competitions achieve localization using a 2D map plotted using adaptive Monte Carlo localization, a robot operating system (ROS) open-source software program. However, outdoor environments often include nonlevel terrain such as slopes. In the indoor environment of multilevel structures, the data representing different levels overlap on the map. These factors can lead to localization failures. To resolve this problem, we develop a software by combining HDL localization, which is an ROS open-source software, with our own program, and use it to achieve localization based on a 3D map. Furthermore, the authors observe the erroneous recognition of a slope as a forward obstacle during a competition event. To resolve this, we propose a method to correct erroneous recognition of obstacles using a 2D laser range finder and 3D map and confirm its validity in an experiment carried out on a slope on a university campus.

Application and Research on Improved Adaptive Monte Carlo Localization Algorithm for Automatic Guided Vehicle Fusion with QR Code Navigation

SLAM (simultaneous localization and mapping) technology incorporating QR code navigation has been widely used in the mobile robotics industry. However, the particle kidnapping problem, positioning accuracy, and navigation time are still urgent issues to be solved. In this paper, a SLAM fused QR code navigation method is proposed and an improved adaptive Monte Carlo positioning algorithm is used to fuse the QR code information. Firstly, the generation and resampling methods of initialized particle swarms are improved to improve the robustness and weights of the swarms and to avoid the kidnapping problem. Secondly, the Gmapping scan data and the data generated by the improved AMCL algorithm are fused using the extended Kalman filter to improve the accuracy and stability of the state estimation. Finally, in terms of the positioning system, Gmapping is used to obtain QR code data as marker positions on static maps, and the improved adaptive Monte Carlo localization particle positioning algorithm is matched with a library of QR code templates, which corrects for offset distances and achieves precise point-to-point positioning under grey-valued raster maps. The experimental results show that the particles encountered with kidnapping can be quickly adjusted in position, with a 68.73% improvement in adjustment time, 64.27% improvement in navigation and positioning accuracy, and 42.81% reduction in positioning time.

Simultaneous Localization and Mapping and Tag-Based Navigation for Unmanned Aerial Vehicles

This paper presents navigation techniques for an Unmanned Aerial Vehicle (UAV) in a virtual simulation of an indoor environment using Simultaneous Localization and Mapping (SLAM) and April Tag markers to reach a target destination. In many cases, UAVs can access locations that are inaccessible to people or regular vehicles in indoor environments, making them valuable for surveillance purposes. This study employs the Robot Operating System (ROS) to simulate SLAM techniques using LIDAR and GMapping packages for UAV navigation in two different environments. In the Tag-based simulation, the input topic for April Tag in ROS is camera images, and the calibration of position with a tag is done through assigning a message to each ID and its marker image. On the other hand, navigation in SLAM was achieved using a global and local planner algorithm. For localization, an Adaptive Monte-Carlo Localization (AMCL) technique has been used to identify factors contributing to inconsistent mapping results, such as heavy computational load, grid mapping accuracy, and inadequate UAV localization. Furthermore, this study analyzed the April Tag-based navigation algorithm, which showed satisfactory outcomes due to its lighter computing requirements. It can be ascertained that by using ROS packages, the simulation of SLAM and Tag-based UAV navigation inside a building can be achieved.

The Design and Simulation of Autonomous Agricultural Vehicle for Orchards

In recent years, agricultural producers have faced challenges due to the uncertainty of labor force access, growing demand for safe, accessible, and high-quality agricultural products, high competition with international producers, and the need to reduce their carbon footprint. To continue their competitive and profitable production, producers must invest in technology and increase efficiency. Autonomous agricultural vehicles are crucial for autonomous processes in orchards, increasing productivity, collecting data for decision-making, reducing operating costs, and carbon footprint. This study focuses on the design and simulation of an autonomous vehicle for orchards. The autonomous vehicle can map the orchards using data from odometry and light detection and ranging (LIDAR) sensors by utilizing Simultaneous Localization and Mapping (SLAM) algorithm, accurately determine its position using the Adaptive Monte Carlo Localization method, and avoid obstacles using the dynamic window approach algorithm. The autonomous vehicle is an original design for netted orchards where GPS cannot work properly and is fully autonomous, requiring no external GPS data. It is expected to provide higher efficiency by reducing environmental pollution, operating expenses, and labor force in practice. The success of the mapping and localization application depends on the update frequency of the position and the number of particles used for localization. A path-planning application was developed to reach the desired position from the autonomous agricultural vehicle's current position on the map. The Dijkstra Algorithm was used for path planning, with the Dynamic Window Approach allowing the robot to escape obstacle. The simulation studies achieved the lowest position error when the vehicle's position was updated at intervals of 2 cm, and a minimum of 500 and a maximum of 2000 particles were used. While the vehicle was moving on a straight obstacle row in the simulation environment, an average localization error of 2.1 cm was obtained. This error is convenient as it enables seamless navigation between tree rows without any collision.

SLAM-based Navigation Technology for Rescue Robots in Post-disaster Scenarios

In order to solve the problems of difficulty and high-risk factor in implementing rescue after disasters, this paper designs an intelligent rescue robot autonomous navigation system based on LIDAR synchronous positioning and map building, with a view to achieving autonomous navigation of robots in complex post-disaster scenarios to complete rescue tasks. Firstly, the autonomous navigation system senses the scene by LiDAR and uses gmapping algorithms to construct a map of the post-disaster environment. Secondly, adaptive Monte Carlo localization algorithm is used to achieve robot localization based on radar and odometer data. Then the robot rescue work path is planned to use the Dijkstra algorithm. And TEB local planning path algorithm is used to control the robot. Finally, to verify the reliability of the autonomous navigation system designed in this paper, the ROS system software framework is used as the basis. The SLAM map construction, global path planning, and local real-time obstacle avoidance are tested practically under the scenario to ensure that the autonomous navigation of the mobile robot meets the requirements.

Research on inspection of machine room by mobile robot

The machine room inspection robot, as a special robot, can locate and navigate in the machine room independently, and carry sensors to detect equipment faults and record instrument data instead of the machine room staff. In this paper, the relevant standards and existing inspection robots at home and abroad were investigated and the overall scheme was determined based on the actual machine room environment. The monitoring background employs B/S architecture, allowing staff to deliver inspection tasks and manage robots from any computer. The motion map is created using the GMapping algorithm, and the topology map for inspection is created based on the inspection task and feasible path. For raster map localization, the adaptive Monte Carlo localization algorithm is used. The functional layer software for the robot is designed based on the inspection function to be performed by the robot and the man-machine interaction with the monitoring background. The findings of this paper's research indicate that the inspection robot system can meet the requirements of computer room autonomous inspection.

NEGL: Lightweight and Efficient Neighborhood Encoding-Based Global Localization for Unmanned Ground Vehicles

This paper proposes a lightweight and efficient Neighborhood Encoding-based Global Localization (NEGL) approach for unmanned ground vehicles (UGVs). To realize the reliable feature description and overcome the restriction of limited Field of View (FOV), neighborhood encoding (NE) scheme is firstly proposed to describe the feature via constructing the structural relationship among neighborhood features. Following that, the reliability of NE scheme is analyzed through the NE-similarity measurement between the priori feature and the detected feature. In addition, the probability model of NEGL is proposed, which is a novel idea based on the priori feature map and simplified through the hierarchical clustering and distance-triggered multiple hypothesis tracking (DT-MHT). Finally, the correct global pose of the vehicle under ambiguous environments is gradually recovered. Comparative experiments using the publicly available datasets and our self-recorded datasets are conducted, and evaluation results show the superior performance of NEGL on success ratio, efficiency, running time and localization accuracy over the adaptive Monte Carlo localization (AMCL), NE+AMCL and Cartographer. Additionally, the experimental results of different FOVs demonstrate NEGL is independent on the range and the direction of the FOV.

An Enhanced Adaptive Monte Carlo Localization for Service Robots in Dynamic and Featureless Environments

An Enhanced Adaptive Monte Carlo Localization for Service Robots in Dynamic and Featureless Environments

Ultra-wide-band-based adaptive Monte Carlo localization for kidnap recovery of mobile robot

In the article, a global localization algorithm based on improved ultra-wide-band-based adaptive Monte Carlo localization is proposed for quick and robust kidnap recovery of mobile robot. First, two ultra-wide-band modules, the tag installed inside the mobile robot and the anchor installed inside charging station, are used to obtain the relative distance between the mobile robot and the charging station. Second, the global grid map is converted into a map with obstacle noise given the ranging accuracy of the ultra-wide-band modules with different obstacles. Third, while the robot is kidnapped, matching grids are screened based on the range information of ultra-wide-band modules and the obstacle noise of the grids. Finally, global localization algorithm is performed based on ultra-wide-band-based adaptive Monte Carlo localization to convert randomly generated particles from the whole map into randomly generated particles in the local map. Experimental results based on gazebo simulation and a real scenario showed that our global localization algorithm based on improved ultra-wide-band-based adaptive Monte Carlo localization not only significantly helped to improve the chances of the robot global pose recovery from lost or kidnapped state but also enabled the robot kidnap recovery with a smaller number of randomly generated particles, thus reducing the time to recover its accurate global localization. The algorithm was also more effective especially for kidnap recovery in a similar and large scenario.

Indoor Localization Method for a Mobile Robot Using LiDAR and a Dual AprilTag

Global localization is one of the important issues for mobile robots to achieve indoor navigation. Nowadays, most mobile robots rely on light detection and ranging (LiDAR) and adaptive Monte Carlo localization (AMCL) to realize their localization and navigation. However, the reliability and performance of global localization only using LiDAR are restricted due to the monotonous sensing feature. This study proposes a global localization approach to improve mobile robot global localization using LiDAR and a dual AprilTag. Firstly, the spatial coordinate system constructed with two neighboring AprilTags is applied as the reference basis for global localization. Then, the robot pose can be estimated by generating precise initial particle distribution for AMCL based on the relative tag positions. Finally, in pose tracking, the count and distribution of AMCL particles, evaluating the certainty of localization, is continuously monitored to update the real-time position of the robot. The contributions of this study are listed as follows. (1) Compared to the localization method only using LiDAR, the proposed method can locate the robot’s position with a few iterations and less computer power consumption. (2) The failure localization issues due to the many similar indoor features can be solved. (3) The error of the global localization can be limited to an acceptable range compared to the result using a single tag.

Integration of Real-Time Semantic Building Map Updating with Adaptive Monte Carlo Localization (AMCL) for Robust Indoor Mobile Robot Localization

A robot can accurately localize itself and navigate in an indoor environment based on information about the operating environment, often called a world or a map. While typical maps describe structural layouts of buildings, the accuracy of localization is significantly affected by non-structural building elements and common items, such as doors, appliances, and furniture. This study enhances the robustness and accuracy of indoor robot localization by dynamically updating the semantic building map with non-structural elements detected by sensors. We propose modified Adaptive Monte Carlo Localization (AMCL), integrating object recognition and map updating into the traditional probabilistic localization. With the proposed approach, a robot can automatically correct errors caused by non-structural elements by updating a semantic building map reflecting the current state of the environment. Evaluations in kidnapped robot and traditional localization scenarios indicate that more accurate and robust pose estimation can be achieved with the map updating capability.

Improved LiDAR Localization Method for Mobile Robots Based on Multi-Sensing

In this paper, we propose a localization method applicable to 3D LiDAR by improving the LiDAR localization algorithm, such as AMCL (Adaptive Monte Carlo Localization). The method utilizes multiple sensing information, including 3D LiDAR, IMU and the odometer, and can be used without GNSS. Firstly, the wheel speed odometer and IMU data of the mobile robot are multi-source fused by EKF (Extended Kalman Filter), and the sensor data obtained after multi-source fusion are used as the motion model to participate in the positional prediction of the particle set in AMCL to obtain the initial positioning information of the mobile robot. Then, the position pose difference values output by AMCL at adjacent moments are substituted into the PL-ICP algorithm as the initial position pose transformation matrix, and the 3D laser point cloud is aligned with the nonlinear system using the PL-ICP algorithm. The three-dimensional laser odometer is obtained by LM (Levenberg--Marquard) iterative solution in the PL-ICP algorithm. Finally, the initial position pose output by AMCL is corrected by the three-dimensional laser odometer, and the AMCL particles are weighted and sampled to output the final positioning result of the mobile robot. Through simulation and practical experiments, it is verified that the improved AMCL algorithm has higher positioning accuracy and stability compared to the AMCL algorithm.