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Outdoor localization is an important issue for many applications, such as autonomous mobile robotics and augmented reality. The purpose of this paper is to propose a budgeted dynamic exclusion heuristic based on signal phase shifts from multiple base stations.

The authors also propose an outdoor localization technique based on the particle filter for data fusion and present an overview of a potential target application of the proposed outdoor localization approach for the blind and visually impaired (BVI).

The combination of multiple sensor data tends to overcome the drawbacks of using one sensor technology in the localization process.

The novelty of the proposed approach stems from its ability to fuse data collected from different sensor technologies to converge to more accurate position estimation.

This paper aims to propose a series of approaches to solve the problem of the mobile robot motion control and autonomous navigation in large-scale outdoor GPS-denied environments.

Based on the model of mobile robot with two driving wheels, a controller is designed and tested in obstacle-cluttered scenes in this paper. By using the priori “topology-geometry” map constructed based on the odometer data and the online matching algorithm of 3D-laser scanning points, a novel approach of outdoor localization with 3D-laser scanner is proposed to solve the problem of poor localization accuracy in GPS-denied environments. A path planning strategy based on geometric feature analysis and priority evaluation algorithm is also adopted to ensure the safety and reliability of mobile robot’s autonomous navigation and control.

A series of experiments are conducted with a self-designed mobile robot platform in large-scale outdoor environments, and the experimental results show the validity and effectiveness of the proposed approach.

The problem of motion control for a differential drive mobile robot is investigated in this paper first. At the same time, a novel approach of outdoor localization with 3D-laser scanner is proposed to solve the problem of poor localization accuracy in GPS-denied environments. A path planning strategy based on geometric feature analysis and priority evaluation algorithm is also adopted to ensure the safety and reliability of mobile robot’s autonomous navigation and control.

This paper aims to study the localization problem for autonomous industrial vehicles in the complex industrial environments. Aiming for practical applications, the pursuit is to build a map-less localization system which can be used in the presence of dynamic obstacles, short-term and long-term environment changes.

The proposed system contains four main modules, including long-term place graph updating, global localization and re-localization, location tracking and pose registration. The first two modules fully exploit the deep-learning based three-dimensional point cloud learning techniques to achieve the map-less global localization task in large-scale environment. The location tracking module implements the particle filter framework with a newly designed perception model to track the vehicle location during movements. Finally, the pose registration module uses visual information to exclude the influence of dynamic obstacles and short-term changes and further introduces point cloud registration network to estimate the accurate vehicle pose.

Comprehensive experiments in real industrial environments demonstrate the effectiveness, robustness and practical applicability of the map-less localization approach.

This paper provides comprehensive experiments in real industrial environments.

The system can be used in the practical automated industrial vehicles for long-term localization tasks. The dynamic objects, short-/long-term environment changes and hardware limitations of industrial vehicles are all considered in the system design. Thus, this work moves a big step toward achieving real implementations of the autonomous localization in practical industrial scenarios.

The purpose of this paper is to describe a general purpose localization system, GRAIL. GRAIL provides real‐time, adaptable, indoor localization for wireless devices.

In order to localize as diverse a set of devices as possible, GRAIL utilizes a centralized, anchor‐based approach. GRAIL defines an abstract data model for various system components to support different physical modalities. The scalable architecture of GRAIL provides maximum flexibility to integrate various localization algorithms.

The authors show through real deployments that GRAIL functions over a variety of physical modalities, networks, and algorithms. Further, the authors found that a centralized solution has critical advantages over distributed implementations for handling privacy concerns.

A key contribution of this system is its universal approach: it can integrate different hardware and software capabilities within a single localization framework. The deployment of such a system in academic and research environments allows researchers to explore issues beyond algorithms and investigate effects in real deployments.

This study aims to investigate if smartphone sensors can be used in an unmanned aerial vehicle (UAV) localization system. With the development of technology, smartphones have been tentatively used in micro-UAVs due to their lightweight, inexpensiveness and flexibility. In this study, a Samsung Galaxy S3 smartphone is selected as an on-board sensor platform for UAV localization in Global Positioning System (GPS)-denied environments and two main issues are investigated: Are the phone sensors appropriate for UAV localization? If yes, what are the boundary conditions of employing them?

Efficient accuracy estimation methodologies for the phone sensors are proposed without using any expensive instruments. Using these methods, one can estimate his phone sensors accuracy at any time without special instruments. Then, a visual-inertial odometry scheme is introduced to evaluate the phone sensors-based path estimation performance.

Boundary conditions of using smartphone in a UAV navigation system are found. Both indoor and outdoor localization experiments are carried out and experimental results validate the effectiveness of the boundary conditions and the corresponding implemented scheme.

With the phone as a payload, UAVs can be further realized in smaller scale at lower cost, which will be used widely in the field of industrial robots.

The purpose of this study is to develop a cost-effective autonomous wheelchair robot navigation method that assists the aging population.

Navigation in outdoor environments is still a challenging task for an autonomous mobile robot because of the highly unstructured and different characteristics of outdoor environments. This study examines a complete vision guided real-time approach for robot navigation in urban roads based on drivable road area detection by using deep learning. During navigation, the camera takes a snapshot of the road, and the captured image is then converted into an illuminant invariant image. Subsequently, a deep belief neural network considers this image as an input. It extracts additional discriminative abstract features by using general purpose learning procedure for detection. During obstacle avoidance, the robot measures the distance from the obstacle position by using estimated parameters of the calibrated camera, and it performs navigation by avoiding obstacles.

The developed method is implemented on a wheelchair robot, and it is verified by navigating the wheelchair robot on different types of urban curve roads. Navigation in real environments indicates that the wheelchair robot can move safely from one place to another. The navigation performance of the developed method and a comparison with laser range finder (LRF)-based methods were demonstrated through experiments.

This study develops a cost-effective navigation method by using a single camera. Additionally, it utilizes the advantages of deep learning techniques for robust classification of the drivable road area. It performs better in terms of navigation when compared to LRF-based methods in LRF-denied environments.

Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed adaptive threshold gradient feature (ATGF) model. A received signal strength indicator (RSSI) model with node estimated features is implicated with localization problem and enhanced with hybrid cumulative approach (HCA) algorithm for node optimizations with distance predicting.

Using a theoretical or empirical signal propagation model, the RSSI (known transmitting power) is converted to distance, the received power (measured at the receiving node) is converted to distance and the distance is converted to RSSI (known receiving power). As a result, the approximate distance between the transceiver node and the receiver may be determined by measuring the intensity of the received signal. After acquiring information on the distance between the anchor node and the unknown node, the location of the unknown node may be determined using either the trilateral technique or the maximum probability estimate approach, depending on the circumstances using federated learning.

Improvisation of localization for wireless sensor network has become one of the prime design features for estimating the different conditional changes externally and internally. One such feature of improvement is observed in this paper, via HCA where each feature of localization is depicted with machine learning algorithms imparting the energy reduction problem for each newer localized nodes in Section 5. All affected parametric features on energy levels and localization problem for newer and extinct nodes are implicated with hybrid cumulative approach as in Section 4. The proposed algorithm (HCA with AGTF) has implicated with significant change in energy levels of nodes which are generated newly and which are non-active for a stipulated time which are mentioned and tabulated in figures and tables in Section 6.

Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed ATGF model. An RSSI model with node estimated features is implicated with localization problem and enhanced with HCA algorithm for node optimizations with distance predicting.

Wheelchair robot navigation in different weather conditions using single camera is still a challenging task. The purpose of this study is to develop an autonomous wheelchair robot navigation method in different weather conditions, with single camera vision to assist physically disabled people.

A road detection method, called dimensionality reduction deep belief neural network (DRDBNN), is proposed for drivable road detection. Due to the dimensionality reduction ability of the DRDBNN, it detects the drivable road area in a short time for controlling the robot in real-time. A feed-forward neural network is used to control the robot for the boundary following navigation using evolved neural controller (ENC). The robot detects road junction area and navigates throughout the road, except in road junction, using calibrated camera and ENC. In road junction, it takes turning decision using Google Maps data, thus reaching the final destination.

The developed method is tested on a wheelchair robot in real environments. Navigation in real environments indicates that the wheelchair robot moves safely from source to destination by following road boundary. The navigation performance in different weather conditions of the developed method has been demonstrated by the experiments.

The wheelchair robot can navigate in different weather conditions. The detection process is faster than that of the previous DBNN method. The proposed ENC uses only distance information from the detected road area and controls the robot for boundary following navigation. In addition, it uses Google Maps data for taking turning decision and navigation in road junctions.

Indoor localization is a key tool for robot navigation in indoor environments. Traditionally, robot navigation depends on one sensor to perform autonomous localization. This paper aims to enhance the navigation performance of mobile robots, a multiple data fusion (MDF) method is proposed for indoor environments.

Here, multiple sensor data i.e. collected information of inertial measurement unit, odometer and laser radar, are used. Then, an extended Kalman filter (EKF) is used to incorporate these multiple data and the mobile robot can perform autonomous localization according to the proposed EKF-based MDF method in complex indoor environments.

The proposed method has experimentally been verified in the different indoor environments, i.e. office, passageway and exhibition hall. Experimental results show that the EKF-based MDF method can achieve the best localization performance and robustness in the process of navigation.

Indoor localization precision is mostly related to the collected data from multiple sensors. The proposed method can incorporate these collected data reasonably and can guide the mobile robot to perform autonomous navigation (AN) in indoor environments. Therefore, the output of this paper would be used for AN in complex and unknown indoor environments.

The accuracy of sensor location estimation influences directly the quality and reliability of services provided by a wireless sensor network (WSN). However, current localization methods may require additional hardware, like global positioning system (GPS), or suffer from inaccuracy like detecting radio signals. It is not proper to add extra hardware in tiny sensors, so the aim is to improve the accuracy of localization algorithms.

The original signal propagation‐based localization algorithm adopts a static attenuation factor model and cannot adjust its modeling parameters in accordance with the local environment. In this paper an adaptive localization algorithm for WSNs that can dynamically adjust ranging function to calculate the distance between two sensors is presented. By adjusting the ranging function dynamically, the location of a sensor node can be estimated more accurately.

The NCTUNs simulator is used to verify the accuracy and analyze the performance of the algorithm. Simulation results show that the algorithm can indeed achieve more accurate localization using just a small number of reference nodes in a WSN.

There is a need to have accurate location information of reference nodes.

This is an effective low‐cost solution for the localization of sensor nodes.

An adaptive localization algorithm that can dynamically adjust ranging function to calculate the distance between two sensors for sensor network deployment and providing location services is described.