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This paper aims to demonstrate a cloud-based version of the improved Monte Carlo localization algorithm with robust orientation estimation (IMCLROE). The purpose of this system is to increase the accuracy and efficiency of indoor robot localization.

The cloud-based IMCLROE is constructed with a cloud–client architecture that distributes computation between servers and a client robot. The system operates in two phases: in the offline phase, two maps are built under the MapReduce framework. This framework allows parallel and even distribution of map information to a cloud database in pre-described formats. In the online phase, an Apache HBase is adopted to calculate a pose in-memory and promptly send the result to the client robot. To demonstrate the efficiency of the cloud-based IMCLROE, a two-step experiment is conducted: first, a mobile robot implemented with a non-cloud IMCLROE and a UDOO single-board computer is tested for its efficiency on pose-estimation accuracy. Then, a cloud-based IMCLROE is implemented on a cloud–client architecture to demonstrate its efficiency on both pose-estimation accuracy and computation ability.

For indoor localization, the cloud-based IMCLROE is much more effective in acquiring pose-estimation accuracy and relieving computation burden than the non-cloud system.

The cloud-based IMCLROE achieves efficiency of indoor localization by using three innovative strategies: firstly, with the help of orientation estimation and weight calculation (OEWC), the system can sort out the best orientation. Secondly, the system reduces computation burden with map pre-caching. Thirdly, the cloud–client architecture distributes computation between the servers and client robot. Finally, the similar energy region (SER) technique provides a high-possibility region to the system, allowing the client robot to locate itself in a short time.

This paper aims to focus on real-world mobile systems, and thus propose relevant contribution to the special issue on “Real-world mobile robot systems”. This work on 3D laser semantic mobile mapping and particle filter localization dedicated for robot patrolling urban sites is elaborated with a focus on parallel computing application for semantic mapping and particle filter localization. The real robotic application of patrolling urban sites is the goal; thus, it has been shown that crucial robotic components have reach high Technology Readiness Level (TRL).

Three different robotic platforms equipped with different 3D laser measurement system were compared. Each system provides different data according to the measured distance, density of points and noise; thus, the influence of data into final semantic maps has been compared. The realistic problem is to use these semantic maps for robot localization; thus, the influence of different maps into particle filter localization has been elaborated. A new approach has been proposed for particle filter localization based on 3D semantic information, and thus, the behavior of particle filter in different realistic conditions has been elaborated. The process of using proposed robotic components for patrolling urban site, such as the robot checking geometrical changes of the environment, has been detailed.

The focus on real-world mobile systems requires different points of view for scientific work. This study is focused on robust and reliable solutions that could be integrated with real applications. Thus, new parallel computing approach for semantic mapping and particle filter localization has been proposed. Based on the literature, semantic 3D particle filter localization has not yet been elaborated; thus, innovative solutions for solving this issue have been proposed. Recently, a semantic mapping framework that was already published was developed. For this reason, this study claimed that the authors’ applied studies during real-world trials with such mapping system are added value relevant for this special issue.

The main problem is the compromise between computer power and energy consumed by heavy calculations, thus our main focus is to use modern GPGPU, NVIDIA PASCAL parallel processor architecture. Recent advances in GPGPUs shows great potency for mobile robotic applications, thus this study is focused on increasing mapping and localization capabilities by improving the algorithms. Current limitation is related with the number of particles processed by a single processor, and thus achieved performance of 500 particles in real-time is the current limitation. The implication is that multi-GPU architectures for increasing the number of processed particle can be used. Thus, further studies are required.

The research focus is related to real-world mobile systems; thus, practical aspects of the work are crucial. The main practical application is semantic mapping that could be used for many robotic applications. The authors claim that their particle filter localization is ready to integrate with real robotic platforms using modern 3D laser measurement system. For this reason, the authors claim that their system can improve existing autonomous robotic platforms. The proposed components can be used for detection of geometrical changes in the scene; thus, many practical functionalities can be applied such as: detection of cars, detection of opened/closed gate, etc. […] These functionalities are crucial elements of the safe and security domain.

Improvement of safe and security domain is a crucial aspect of modern society. Protecting critical infrastructure plays an important role, thus introducing autonomous mobile platforms capable of supporting human operators of safe and security systems could have a positive impact if viewed from many points of view.

This study elaborates the novel approach of particle filter localization based on 3D data and semantic mapping. This original work could have a great impact on the mobile robotics domain, and thus, this study claims that many algorithmic and implementation issues were solved assuming real-task experiments. The originality of this work is influenced by the use of modern advanced robotic systems being a relevant set of technologies for proper evaluation of the proposed approach. Such a combination of experimental hardware and original algorithms and implementation is definitely an added value.

The purpose of this paper is to propose an enhanced pose tracking method using progressive scan matching, focusing on accuracy, time efficiency and robustness.

The general purpose of localization algorithms is to dynamically track a robot instead of globally locating one. In this paper, progressive scan matching is used to promote the performance of pose tracking. Rotational and translational samples are separately generated to accelerate the calculation and to increase the accuracy. Progressive iteration of sample generation can ensure localization to achieve a specific precision. The direction of localization uncertainty is taken into consideration to increase robustness. Nonlinear optimization is adopted to achieve a more precise result.

The proposed method was implemented on a self-made mobile robot. Two experiments were conducted to test the accuracy and time efficiency of the method. The comparison with the basic Monte Carlo localization shows the advantages of the method. Another two experiments were conducted to test the robustness of the method. The result shows that the method can relocate a robot from an inaccurate place if the offset is moderate.

An enhanced pose tracking method is proposed to promote the performance by separately processing rotational and translational samples, progressively iterating the sample generation, taking the direction of localization uncertainty into consideration and adopting nonlinear optimization. The proposed method enables a robot to accurately and quickly locate itself in the environment with robustness.

Self-localization of an underwater robot using global positioning sensor and other radio positioning systems is not possible, as an alternative onboard sensor-based self-location estimation provides another possible solution. However, the dynamic and unstructured nature of the sea environment and highly noise effected sensory information makes the underwater robot self-localization a challenging research topic. The state-of-art multi-sensor fusion algorithms are deficient in dealing of multi-sensor data, e.g. Kalman filter cannot deal with non-Gaussian noise, while parametric filter such as Monte Carlo localization has high computational cost. An optimal fusion policy with low computational cost is an important research question for underwater robot localization.

In this paper, the authors proposed a novel predictive coding-biased competition/divisive input modulation (PC/BC-DIM) neural network-based multi-sensor fusion approach, which has the capability to fuse and approximate noisy sensory information in an optimal way.

Results of low mean localization error (i.e. 1.2704 m) and computation cost (i.e. 2.2 ms) show that the proposed method performs better than existing previous techniques in such dynamic and unstructured environments.

To the best of the authors’ knowledge, this work provides a novel multisensory fusion approach to overcome the existing problems of non-Gaussian noise removal, higher self-localization estimation accuracy and reduced computational cost.

Robot localization technology has been widely studied for decades and a lot of remarkable approaches have been developed. However, in practice, this technology has hardly been applied to common day-to-day deployment scenarios. The purpose of this paper is to present a novel approach that focuses on improving the localization robustness in complicated environment.

The localization robustness is improved by dynamically switching the localization components (such as the environmental camera, the laser range finder and the depth camera). As the components are highly heterogeneous, they are developed under the robotic technology component (RTC) framework. This simplifies the developing process by increasing the potential for reusability and future expansion. To realize this switching, the localization reliability for each component is modeled, and a configuration method for dynamically selecting dependable components at run-time is presented.

The experimental results show that this approach significantly decreases robot lost situation in the complicated environment. The robustness is further enhanced through the cooperation of heterogeneous localization components.

A multi-component automatic switching approach for robot localization system is developed and described in this paper. The reliability of this system is proved to be a substantial improvement over single-component localization techniques.

This study aims to present an automated guided logistics robot mainly designed for pallet transportation. Logistics robot is compactly designed. It could pick up the pallet precisely and transport the pallet up to 1,000 kg automatically in the warehouse. It could move freely in all directions without turning the chassis. It could work without any additional infrastructure based on laser navigation system proposed in this work.

Logistics robot should be able to move underneath and lift up the pallet accurately. Logistics robot mainly consists of two sub-robots, like two forks of the forklift. Each sub-robot has front and rear driving units. A new compact driving unit is compactly designed as a key component to ensure access to the narrow free entry of the pallet. Besides synchronous motions in all directions, the two sub-robots should also perform synchronous lifting up and laying down the pallet. Logistics robot uses a front laser to detect obstacles and locate itself using on-board navigation system. A rear laser is used to recognize and guide the sub-robots to pick up the pallet precisely within ± 5mm/1o in x-/yaw direction. Path planning algorithm under different constraints is proposed for logistics robot to obey the traffic rules of pallet logistics.

Compared with the traditional forklift vehicles, logistics robot has the advantages of more compact structure and higher expandability. It can realize the omnidirectional movement flexibly without turning the chassis and take zero-radius turn by controlling compact driving units synchronously. Logistics robot can move collision-free into any pallet that has not been precisely placed. It can plan the paths for returning to charge station and charge automatically. So it can work uninterruptedly for 7 × 24 h. Path planning algorithm proposed can avoid traffic congestion and improve the passability of the narrow roads to improve logistics efficiencies. Logistics robot is quite suitable for the standardized logistics factory with small working space.

This is a new innovation for pallet transportation vehicle to improve logistics automation.

The purpose of this paper is to address the online localization of mobile (service) robots in real world dynamic environments. Most of the techniques developed so far have been designed for static environments. What is presented here is a novel technique for mobile robot localization in quasi‐dynamic environments.

The proposed approach employs a probability grid map and Baye's filtering techniques. The former is used for representing the possible changes in the surrounding environment which a robot might have to face.

Simulation and experimental results show that this approach has a high degree of robustness by taking into account both sensor and world uncertainty. The methodology has been tested under different environment scenarios where diverse complex objects having different sizes and shapes were used to represent movable and non‐movable entities.

The results can be applied to diverse robotic systems that need to move in changing indoor environments such as hospitals and places where people might require assistance from autonomous robotic devices. The methodology is fast, efficient and can be used in fast‐moving robots, allowing them to perform complex operations such as path planning and navigation in real time.

What is proposed here is a novel mobile robot localization approach that enables unmanned vehicles to effectively move in real time and know their current location in dynamic environments. Such an approach consists of two steps: a generation of the probability grid map; and a recursive position estimation methodology employing a variant of the Baye's filter.

The purpose of this paper is to propose a localizability-based particle filtering localization algorithm for mobile robots to maintain localization accuracy in the high-occluded and dynamic environments with moving people.

First, the localizability of mobile robots is defined to evaluate the influences of both the dynamic obstacles and prior-map on localization. Second, based on the classical two-sensor track fusion algorithm, the odometer-based proposal distribution function (PDF) is corrected, taking account of the localizability. Then, the corrected PDF is introduced into the classical PF with “roulette” re-sampling. Finally, the robot pose is estimated according to all the particles.

The experimental results show that, first, it is necessary to consider the influence of the prior-map during the localization in the high-occluded and dynamic environments. Second, the proposed algorithm can maintain an accurate and robust robot pose in the high-occluded and dynamic environments. Third, its real timing is acceptable.

When the odometer error and occlusion caused by the dynamic obstacles are both serious, the proposed algorithm also has a probability evolving into the kidnap problem. But fortunately, such serious situations are not common in practice.

To check the ability of real application, we have implemented the proposed algorithm in the campus cafeteria and metro station using an intelligent wheelchair. To better help the elderly and disabled people during their daily lives, the proposed algorithm will be tested in a social welfare home in the future.

The localizability of mobile robots is defined to evaluate the influences of both the dynamic obstacles and prior-map on localization. Based on the localizability, the odometer-based PDF is corrected properly.

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.

One difficult problem in robotics is localization: the ability of a mobile robot to determine its position in the environment. Roboticists around the globe have been working to find a solution to localization for more than 20 years; however, only in the past 4‐5 years we have seen some promising results. In this work, we describe a first‐of‐a‐kind, breakthrough technology for localization that requires only one low‐cost camera (less than 50USD) and odometry to provide localization. Because of its low‐cost and robust performance in realistic environments, this technology is particularly well‐suited for use in consumer and commercial applications.