Mobile Robot Localization Research Articles

Integrating synthetic datasets with CLIP semantic insights for single image localization advancements

Accurate localization of pedestrians and mobile robots is critical for navigation, emergency response, and autonomous driving. Traditional localization methods, such as satellite signals, often prove ineffective in certain environments, and acquiring sufficient positional data can be challenging. Single image localization techniques have been developed to address these issues. However, current deep learning frameworks for single image localization that rely on domain adaptation fail to effectively utilize semantically rich high-level features obtained from large-scale pretraining. This paper introduces a novel framework that leverages the Contrastive Language-Image Pre-training model and prompts to enhance feature extraction and domain adaptation through semantic information. The proposed framework generates an integrated score map from scene-specific prompts to guide feature extraction and employs adversarial components to facilitate domain adaptation. Furthermore, a reslink component is incorporated to mitigate the precision loss in high-level features compared to the original data. Experimental results demonstrate that the use of prompts reduces localization errors by 26.4 % in indoor environments and 24.3 % in outdoor settings. The model achieves localization errors as low as 0.75 m and 8.09 degrees indoors, and 4.56 m and 7.68 degrees outdoors. Analysis of prompts from labeled datasets confirms the model’s capability to effectively interpret scene information. The weights of the integrated score map enhance the model’s transparency, thereby improving interpretability. This study underscores the efficacy of integrating semantic information into image localization tasks.

Proposal of simultaneous localization and mapping for mobile robots indoor environments using Petri nets and computer vision

Proposal of simultaneous localization and mapping for mobile robots indoor environments using Petri nets and computer vision

Single-Beacon Localization for Mobile Robot: A Set Membership Filtering Approach

Single-Beacon Localization for Mobile Robot: A Set Membership Filtering Approach

Quantifying mobile robot localization safety for an EKF-based SLAM estimator: An integrity monitoring approach

The safety of SLAM-based estimation for mobile robots is a challenging research problem, particularly for life- or mission-critical exploratory applications. To address this problem, this work proposes a method to utilize integrity risk, a widely used performance metric in aviation, to quantify SLAM-based mobile robot’s localization safety. More importantly, the approach accounts for sensor measurement faults, unknown deterministic errors that cannot be modeled via Gaussian white noise. The method is tailored for an EKF-based SLAM estimator, a chi-squared failure detector, and a local nearest neighbors data association criterion. The results show that data association errors can cause significant positioning performance degradation that can only be predicted using the proposed integrity risk metric. Furthermore, the study demonstrates that as the map’s landmark density increases, mobile robot localization safety improves, except when the landmark map’s density is too high to make the features indistinguishable, which leads to positioning safety degradation.

Secure Event-Triggered Distributed Finite-Memory Localization for Multiple Mobile Robots Under Cyberattacks

Secure Event-Triggered Distributed Finite-Memory Localization for Multiple Mobile Robots Under Cyberattacks

Real-Time Submap Trimming-Based Map Updating for Long-Term Localization of Mobile Robot in Dynamic Environments

Real-Time Submap Trimming-Based Map Updating for Long-Term Localization of Mobile Robot in Dynamic Environments

Indoor Localization of Mobile Robots using Inertial Data and Deep Learning

Indoor Localization of Mobile Robots using Inertial Data and Deep Learning

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.

Cost-Effective Localization of Mobile Robots Using Ultrasound Beacons and Differential Time-of-Flight Measurement

This paper presents an innovative and cost-effective solution for the absolute localization of mobile robots using ultrasound beacons. The proposed system addresses the challenge of precise positioning within a controlled environment by employing Differential Time-of-Flight (ToF) measurements to determine the relative distances between the robot and optimally placed beacons. Unlike other ToF methods that require synchronization pulses, the proposed approach eliminates this requirement, significantly simplifying the setup and reducing system complexity. Furthermore, the system achieves a higher sampling rate than conventional synchronization-based systems, enhancing real-time performance. Detailed analysis and simulation demonstrate the system’s ability to provide accurate and reliable localization. The results highlight the potential for broad application in various robotic environments, offering a robust solution for absolute positioning without complex synchronization strategies. This work underscores the advantages of using ToF measurements with ultrasound beacons and contributes to the ongoing development of efficient and cost-effective robotic localization systems.

Fiducial Markers and Particle Filter Based Localization and Navigation Framework for an Autonomous Mobile Robot

Fiducial Markers and Particle Filter Based Localization and Navigation Framework for an Autonomous Mobile Robot

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.

Event‐triggered resilient joint mobile robot localization and sensor fault estimation

SummaryThe event‐triggered joint sensor fault estimation and mobile robot (MR) localization (MRL) problem (MRLP) subject to the potential fluctuations of the estimator gain are investigated. From the external sensor to the estimator, to decrease the consumption of sensor energy and cut down network bandwidth resources, an event‐triggered scheme is considered. For the sake of characterizing the phenomenon induced by the inaccurate calculation of estimator gain, an uncertainty with the bounded second moment is employed. The purpose of this study is theoretically to find a feasible and effective approach to the addressed joint estimation problem such that the estimation error (EE) covariance (EEC) meets the given performance index. First, a minimum upper bound (UB) of the EEC is derived. Subsequently, in terms of the proposed joint estimation approach and the corresponding results obtained, an algorithm to address the resilient joint sensor fault estimation and MRLP is summarized. At the end, the effectiveness of the proposed algorithm is validated through conducting a set of comparisive experiments.

Source localization based on field signatures: Laboratory ultrasonic validation

Location awareness in wireless networks is essential for emergency services, navigation, gaming and many other applications. This article presents a method for source localization based on measuring the amplitude-phase distribution of the field at the base station. The existing scatterers in the target area create unique scattered field interference at each source location. The unique field interference at each source location results in a unique field signature at the base station which is used for source localization. In the proposed method, the target area is divided into a grid with a step of less than half the wavelength. Each grid node is characterized by its field signature at the base station. Field signatures corresponding to all nodes are normalized and stored in the base station as fingerprints for source localization. The normalization of the field signatures avoids the need for time synchronization between the base station and the source. When a source transmits signals, the generated field signature at the base station is normalized and then correlated with the stored fingerprints. The maximum correlation value is given by the node to which the source is the closest. Numerical simulations and results of experiments on ultrasonic waves in the air show that the ultrasonic source is correctly localized using broadband field signatures with one base station and without time synchronization. The proposed method is potentially applicable for indoor localization and navigation of mobile robots.

Uncertainty‐aware LiDAR‐based localization for outdoor mobile robots

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

A fast point cloud registration method based on spatial relations and features

Abstract Point cloud registration plays a crucial role in mobile robot localization, map building and three-dimensional (3D) model reconstruction. However, it remains challenged by issues such as compromised accuracy and sluggish efficiency, posing significant obstacles in achieving precise and timely alignments. Therefore, we propose a lightweight and fast point cloud registration method. Firstly, we mesh the 3D point cloud, compared with the traditional gridded point cloud method, it achieves initial point cloud registration by preserving the curvature characteristics of the internal point cloud, and utilizing the spatial relationship between grid cells and the quantitative relationship between the internal point cloud. Moreover, we adopt an iterative nearest point based on KD-Tree to realize the fine registration. So, our method does not necessitate intricate feature analysis and data training, and is resilient to similar transformations, non-uniform densities and noise. Finally, we conduct point cloud registration experiments using multiple publicly available point cloud datasets and compare them with several point cloud registration methods. The results demonstrate it is able to accomplish the point cloud registration quickly and exhibit high accuracy. More importantly, it maintains its efficacy and robustness even in the presence of noisy and defective point clouds.

An evaluation of CNN models and data augmentation techniques in hierarchical localization of mobile robots

This work presents an evaluation of CNN models and data augmentation to carry out the hierarchical localization of a mobile robot by using omnidirectional images. In this sense, an ablation study of different state-of-the-art CNN models used as backbone is presented and a variety of data augmentation visual effects are proposed for addressing the visual localization of the robot. The proposed method is based on the adaption and re-training of a CNN with a dual purpose: (1) to perform a rough localization step in which the model is used to predict the room from which an image was captured, and (2) to address the fine localization step, which consists in retrieving the most similar image of the visual map among those contained in the previously predicted room by means of a pairwise comparison between descriptors obtained from an intermediate layer of the CNN. In this sense, we evaluate the impact of different state-of-the-art CNN models such as ConvNeXt for addressing the proposed localization. Finally, a variety of data augmentation visual effects are separately employed for training the model and their impact is assessed. The performance of the resulting CNNs is evaluated under real operation conditions, including changes in the lighting conditions. Our code is publicly available on the project website https://github.com/juanjo-cabrera/IndoorLocalizationSingleCNN.git.

Study on the indoor mobile robot localization based on multi-sensor fusion

Study on the indoor mobile robot localization based on multi-sensor fusion

Indoor Mobile Robot Localization Applying IMU/Stereo Camera/LiDAR and Graph-Based Optimization

Indoor Mobile Robot Localization Applying IMU/Stereo Camera/LiDAR and Graph-Based Optimization

Stereo visual-inertial localization algorithm for orchard robots based on point-line features

The vision-based localization of orchard mobile robots is an indispensable component of orchard intelligent management, applicable to scenarios such as navigation and harvesting. Due to the rugged terrain and lack of texture features in orchard environments, the accuracy of Visual-Inertial Odometry (VIO) based on feature point is compromised, making it challenging to meet practical application requirements. Addressing these issues, this paper proposes an algorithm for stereo visual-inertial localization based on point-line features, by enhancing VINS-Fusion. The algorithm incorporates the line feature detector algorithm (LSD) into the odometry front-end to detect line features, addressing the insufficient effective feature points and low-quality feature matching issues in orchard environments. Simultaneously, strategies for length filtering and feature matching are introduced for line features, eliminating poor-quality line features and matches caused by rugged terrain, thereby reducing adverse impacts on localization accuracy. To prevent keyframe redundancy due to a scarcity of stable feature points in areas with missing textures, such as tree trunks and leaves, a keyframe criteria optimization method based on point-line features is designed. Back-end optimization minimizes the cost function containing residuals of point-line features, prior information, and IMU, ultimately improving the localization accuracy of mobile robots. To validate the effectiveness and practicality of the proposed algorithm, a self-built dataset was created in real orchard environments, including scenes from banana plantation, mango orchard, and wampee orchard. Localization experiments were conducted using Rosario, EuRoC public datasets and self-built orchard dataset. Results indicate that the proposed algorithm consistently achieves lower ATE-RMSE than VINS-Fusion, with a maximum reduction of 79.2 % in the Rosario dataset sequence, 23.3 % in the EuRoC dataset sequence, and ATE-RMSE values of 0.091 m, 0.107 m, and 0.093 m in banana plantation, mango orchard, and wampee orchard, respectively. These represent reductions of 22.9 %, 7.6 %, and 27.3 % compared to VINS-Fusion. In the self-built orchard dataset, the average frame processing times for the algorithm front-end is 56.3 ms, while for the back-end is 31 ms, meeting the practical localization requirements. The proposed algorithm effectively enhances the localization accuracy of orchard mobile robots, offering a viable solution for autonomous navigation in orchard robot systems.

Localization of mobile robot in prior 3D LiDAR maps using stereo image sequence

Localization of mobile robot in prior 3D LiDAR maps using stereo image sequence