Mobile Robot Localization System Research Articles

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

Localization of Mobile Robots Based on Depth Camera

In scenarios of indoor localization of mobile robots, Global Positioning System (GPS) signals are prone to loss due to interference from urban building environments and cannot meet the needs of robot localization. On the other hand, traditional indoor localization methods based on wireless signals such as Bluetooth and WiFi often require the deployment of multiple devices in advance, and these methods can only obtain distance information and are unable to obtain the attitude of the positioning target in space. This paper proposes a method for the indoor localization of mobile robots based on a depth camera. Firstly, we extracted ORB feature points from images captured by a depth camera and performed homogenization processing. Then, we performed feature matching between adjacent two frames of images, and the mismatched points are eliminated to improve the accuracy of feature matching. Finally, we used the Iterative Closest Point (ICP) algorithm to estimate the camera’s pose, thus achieving the localization of mobile robots in indoor environments. In addition, an experimental evaluation was conducted on the TUM dataset of the Technical University of Munich to validate the feasibility of the proposed depth-camera-based indoor localization system for mobile robots. The experimental results show that the average localization accuracy of our algorithm on three datasets is 0.027 m, which can meet the needs of indoor localization for mobile robots.

RFID-enabled localization system for mobile robot in the workshop

The development of RFID enabled intelligent localization system in the workshop is of great importance for reducing the operation cost, increasing production efficiency and improving management capabilities. From the aspects of feature extraction of RF fingerprint and localization algorithm, the scheme of mobile target tracking based on the fusion of inertial navigation and fingerprinting is explored. The deep neural network is used to establish the nonlinear relationship between fingerprint and coordinates. After the initial position of the mobile robot is obtained, Kalman filter is used to fuse the data collected by IMU and wheel encoder. Experimental results show that the proposed method is feasible and be able to track the mobile robot accurately.

Localization System for Vehicle Navigation Based on GNSS/IMU Using Time-Series Optimization with Road Gradient Constrain

In this paper, we propose a GNSS/IMU localization system for mobile robots when wheel speed sensors cannot be attached. Highly accurate location information is required for autonomous navigation of mobile robots. A typical method of acquiring location information is to use a Kalman filter for position estimation. The Kalman filter is a maximum-likelihood estimation method that assumes normally distributed noise. However, non-normally distributed GNSS multipath noise that frequently occurs in urban environments causes the Kalman filter to break down, and degrades the estimation performance. Other GNSS/IMU localization methods capable of lane-level estimation in urban environments use wheel speed sensors, which are unsuitable for the present situation. In this study, we aim to improve the performance of lane-level localization by adding a vehicle speed estimation function to adapt the method to those requiring wheel speed sensors. The proposed method optimizes time-series data to accurately compensate for accelerometer bias errors and reduce GNSS multipath noise. The evaluation confirmed the effectiveness of the proposed method, with improved velocity and position estimation performance compared with the Kalman filter method.

Factor graph localization for mobile robots using Google Indoor Street View and CNN-based place recognition

This article proposes a mobile robot localization system developed using Google Indoor Street View (GISV) and Convolutional Neural Network (CNN)-based visual place recognition. The proposed localization system consists of two main modules. The first is a place recognition module based on GISV and a net Vector of Locally Aggregated Descriptors (VLAD)-based CNN. The second is a factor graph-based optimization module. In this work, we show that a CNN-based approach can be utilized to overcome the lack of visually distinct features in indoor environments and changes in images that can occur when using different cameras at different points in time for localization. The proposed CNN-based localization system is implemented using reference and query images obtained from two different sources (GISV and a camera attached to a mobile robot). It has been experimentally validated using a custom indoor dataset captured at the Memorial University of Newfoundland engineering building basement. The main results of this paper show that GISV-based place recognition reduces the percentage drift by 4% for the dataset and achieves a Root Mean Square Error (RMSE) of 2 m for position and 2.5° for orientation.

High-precision and robust localization system for mobile robots in complex and large-scale indoor scenes

High-precision and robust localization is the key issue for long-term and autonomous navigation of mobile robots in industrial scenes. In this article, we propose a high-precision and robust localization system based on laser and artificial landmarks. The proposed localization system is mainly composed of three modules, namely scoring mechanism-based global localization module, laser and artificial landmark-based localization module, and relocalization trigger module. Global localization module processes the global map to obtain the map pyramid, thus improve the global localization speed and accuracy when robots are powered on or kidnapped. Laser and artificial landmark-based localization module is employed to achieve robust localization in highly dynamic scenes and high-precision localization in target areas. The relocalization trigger module is used to monitor the current localization quality in real time by matching the current laser scan with the global map and feeds it back to the global localization module to improve the robustness of the system. Experimental results show that our method can achieve robust robot localization and real-time detection of the current localization quality in indoor scenes and industrial environment. In the target area, the position error is less than 0.004 m and the angle error is less than 0.01 rad.

Improving Tightly LiDAR/Compass/Encoder-Integrated Mobile Robot Localization with Uncertain Sampling Period Utilizing EFIR Filter

In order to overcome the uncertainty of the data sampling period of the sensor due to equipment reasons, a mobile robot localization system is developed under the uncertain sampling period for the tightly-fused light detection and ranging (LiDAR), compass, and encoder data. The errors of position and velocity, the robot’s yaw, and the sampling period are chosen as state variables. The ranges between the corner feature points (CFPs) and the mobile robot measured by the LiDAR, compass, and encoder are considered as an observation. Based on the tightly-integrated nonlinear model, the extended unbiased finite-impulse response (EFIR) filter fuses the sensors’ data for the integrated localization system. The performances of the traditional loosely-coupled integration scheme, tightly-coupled integration scheme with a constant sampling interval, and tightly-coupled integration with an uncertain sampling interval are compared based on real data. It is shown experimentally that the proposed scheme is more accurate then the traditional loosely-coupled integration and the one relying on a constant sampling interval, which improves by about 10.2%.

Accurate real-time visual SLAM combining building models and GPS for mobile robot

This paper presents a novel 7 DOF (i.e., orientation, translation, and scale) visual simultaneous localization and mapping (vSLAM) system for mobile robots in outdoor environments. In the front end of this vSLAM system, a fast initialization method is designed for different vSLAM backbones, which upgrades the accuracy of trajectory and reconstruction of vSLAM with an absolute scale computed from depth maps generated by building blocks. In the back end of this vSLAM, we propose a nonlinear optimization mechanism throughout which multimodal data are combined for more robust optimization. The modality of building blocks in optimization can improve the tracking accuracy and the scale estimation. By integrating the pose estimated from visual information and the position received through GPS, the optimization further alleviates the drift. The experimental results prove that the proposed method is extremely suitable for outer AR application for outdoor environments, because our method has superior initialization performance, runs in real time, and achieves real scale, higher accuracy, and robustness.

Design of a flexible reconfigurable mobile robot localization system using FPGA technology

Mobile robot activities have variable computational requirements according to various parameters related to the robotic applications behaviour and the robot dynamic environment. This high variability puts many challenges for a complete robot navigation system design. With their high flexibility, reconfigurable architectures are a suitable choice to design such dynamic system. Mobile robot navigation steps (perception, localization, obstacle avoidance and path planning) are extremely complex to be embedded in a complete navigation controller. In this paper, we presented a generic and a flexible platform based on FPGA technology, for studying and prototyping complete unified robot navigation system. We exploited the dynamic reconfiguration mechanism that allows modifying the navigation controller according to different navigation algorithm requirements. We showed the effectiveness of this platform in the localization step. We have a dynamic reconfigurable system that switches between three different localization techniques to satisfy energy/surface ratio and exploit the released resources to implement other robot tasks.

Research of visual simultaneous localization and mapping-based navigation system for mobile robots

Subject of Research. The paper considers navigation system for mobile robots by building a map of the environment using Simultaneous Localization and Mapping algorithm and converting the 3D map collected by INTEL Realsense Depth camera into a 2D-cost map. Method. Real-Time Appearance-Based Mapping was used for building a virtual 3D map of the environment. A binary map was obtained by projecting the 3D map on a plane. The D* algorithm was applied on the binary map for planning a global path to the goal. The Dynamic-Window Approach was used as a local planner. Main Results. A point cloud of the environment was created and converted to a 2D map. A robot was safely navigated to the desired location. Practical Relevance. The proposed approach is fast and reliable and can be used for indoor navigation (factories and companies). Since the map needs to be designed only once, the calculation can be handled by CPU without any need for graphics processing unit.

A low cost indoor localization system for mobile robot experimental setup

Indoor localization becomes one of the most important part in mobile robot system One fundamental requirement is to provide an easy-to-use and practical localization system for real-time experiments. In this paper we propose a combination of a recent open source virtual reality (VR) tools, a simple MATLAB code and a low cost USB webcam as an indoor mobile robot localization system Using the VR tools as a server and MATLAB as a client, the proposed solution can cover up to 1.6 [m] × 3.2 [m] with the measurement position accuracy up to 1.2 [cm]. The system is insensitive to light, easy to move and can be quickly set up. A series of successful real-time experiments with three different mobile robot types has been conducted.

An improved graph-based visual localization system for indoor mobile robot using newly designed markers

To address the localization problem for a mobile robot in an indoor environment, an improved visual localization system based on artificial markers is proposed in this article. First, we will prese...

Localization System for Autonomous Mobile Robots Using Machine Learning Methods and Omnidirectional Sonar

In the past few years, the mobile robotics has presented a growth in the research field, due to its high application area. The robot localization and its displacement in an unknown environment are fundamental to its operation. Signal processing techniques, machine learning methods, and sensors assist in the environment mapping, localization, and displacement of the mobile robot in the environment. In this paper, five machine learning techniques used: Bayes, MLP, KNN, SVM and OPF. We have used the following evaluation metrics: accuracy, sensitivity, and specificity obtained through matrices of confusion. This paper has as certain objective the analysis of the machine learning methods for later use in an embedded system. The OPF method using Canberra distance obtained an accuracy of 98.13% and is, therefore, the most appropriate method to use on the proposed location system.

Robust mobile robot localization based on a security laser: an industry case study

Purpose This paper aims to address a mobile robot localization system that avoids using a dedicated laser scanner, making it possible to reduce implementation costs and the robot’s size. The system has enough precision and robustness to meet the requirements of industrial environments. Design/methodology/approach Using an algorithm for artificial beacon detection combined with a Kalman Filter and an outlier rejection method, it was possible to enhance the precision and robustness of the overall localization system. Findings Usually, industrial automatic guide vehicles feature two kinds of lasers: one for navigation placed on top of the robot and another for obstacle detection (security lasers). Recently, security lasers extended their output data with obstacle distance (contours) and reflectivity. These new features made it possible to develop a novel localization system based on a security laser. Research limitations/implications Once the proposed methodology is completely validated, in the future, a scheme for global localization and failure detection should be addressed. Practical implications This paper presents a comparison between the presented approach and a commercial localization system for industry. The proposed algorithms were tested in an industrial application under realistic working conditions. Social implications The presented methodology represents a gain in the effective cost of the mobile robot platform, as it discards the need for a dedicated laser for localization purposes. Originality/value This paper presents a novel approach that benefits from the presence of a security laser on mobile robots (mandatory sensor when considering industrial applications), using it simultaneously with other sensors, not only to guarantee safety conditions during operation but also to locate the robot in the environment. This paper is also valuable because of the comparison made with a commercialized system, as well as the tests conducted in real industrial environments, which prove that the approach presented is suitable for working under these demanding conditions.

Development of an intelligent mobile robot localization system using Kinect RGB-D mapping and neural network

The most important issue of intelligent mobile robot development is to navigate autonomously in the environment for completing certain task demands. Nowadays, the Kinect sensor is affordable and popular for acquiring environment RGB image pixels with depth estimation. In this study, we focus on developing the indoor localization system for intelligent mobile robot applications. The innovation of this research is to combine the RGB-D mapping and neural network training for achieving an Indoor Positioning System. It is expected that the inputs are the robot’s observations of environmental features / landmarks and the direct output is the robot’s posture which will correspond to the RGB-D map. All the experimental results suggest that the robot’s posture and localization adjusts very efficiently with this study’s proposed method.

Optimal Camera Placement of Large Scale Volume Localization System for Mobile Robot

Large Scale Volume Localization System (LSVLS) is applied widely in industry. Large Scale Volume Localization System with camera network has appropriate precise and cost, which is a promising system in metrology and localization in industry and lives. Optimal camera placement is significant to lower cost and facilitate target’s auto-control for mobile robot in the large workspace. The author optimized cameras placement with their relative position algorithm (RPA). The result of optimal camera placement enhances greatly the efficiency of camera placement in LSVLS and is verified with a model of field-winding mobile vehicle.

Global Appearance Applied to Visual Map Building and Path Estimation Using Multiscale Analysis

In this work we present a topological map building and localization system for mobile robots based on global appearance of visual information. We include a comparison and analysis of global-appearance techniques applied to wide-angle scenes in retrieval tasks. Next, we define multiscale analysis, which permits improving the association between images and extracting topological distances. Then, a topological map-building algorithm is proposed. At first, the algorithm has information only of some isolated positions of the navigation area in the form of nodes. Each node is composed of a collection of images that covers the complete field of view from a certain position. The algorithm solves the node retrieval and estimates their spatial arrangement. With these aims, it uses the visual information captured along some routes that cover the navigation area. As a result, the algorithm builds a graph that reflects the distribution and adjacency relations between nodes (map). After the map building, we also propose a route path estimation system. This algorithm takes advantage of the multiscale analysis. The accuracy in the pose estimation is not reduced to the nodes locations but also to intermediate positions between them. The algorithms have been tested using two different databases captured in real indoor environments under dynamic conditions.

Mobile robot localization and navigation system based on monocular vision

A system for mobile robot localization and navigation was presented. With the proposed system, the robot can be located and navigated by a single landmark in a single image. And the navigation mode may be following-track, teaching and playback, or programming. The basic idea is that the system computes the differences between the expected and the recognized position at each time and then controls the robot in a direction to reduce those differences. To minimize the robot sensor equipment, only one omnidirectional camera was used. Experiments in disturbing environments show that the presented algorithm is robust and easy to implement, without camera rectification. The root-mean-square error (RMSE) of localization is 1.4 cm, and the navigation error in teaching and playback is within 10 cm.

A HYBRID SENSING APPROACH TO MOBILE ROBOT LOCALIZATION IN COMPLEX INDOOR ENVIRONMENTS

This paper presents a hybrid sensing system for mobile robot localization in large-scale indoor environments. The system operates in two sensing modes, either omni-directional vision or laser scanning, according to the environmental characteristics. For a structured corridor environment, the vision information is adopted to track the robot pose with a predefined hybrid metric-topological map. For a semi-structured office room, the laser scanning mode is chosen to generate a sequence of relative pose transformations based on a scan matching algorithm. Kalman filters are deployed to smooth multiple scan matching results. The proposed hybrid sensing system can perform localization tasks on-the-fly, with the features of efficient map modelling and computational simplicity. Experimental results are provided to demonstrate the performance and effectiveness of the proposed techniques.

A dead reckoning localization system for mobile robots using inertial sensors and wheel revolution encoding

Inertial navigation systems (INS) are composed of inertial sensors, such as accelerometers and gyroscopes. An INS updates its orientation and position automatically; it has an acceptable stability over the short term, however this stability deteriorates over time. Odometry, used to estimate the position of a mobile robot, employs encoders attached to the robot’s wheels. However, errors occur caused by the integrative nature of the rotating speed and the slippage between the wheel and the ground. In this paper, we discuss mobile robot position estimation without using external signals in indoor environments. In order to achieve optimal solutions, a Kalman filter that estimates the orientation and velocity of mobile robots has been designed. The proposed system combines INS and odometry and delivers more accurate position information than standalone odometry.