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This paper aims to present a novel localization scheme using infrared light reflecting artificial landmarks.

By putting the infrared light reflecting landmarks on the ceiling, localization is achieved. The landmark is designed for effective recognition and identification. From the difference of two successive images, one with the infrared light illumination and the other without, the landmarks are clearly identified.

From the mathematical analysis, a greater of landmarks are required if the robot's tilt angles are not known. With the camera's pan/tilt angles information, the distortion of the image can be corrected and fewer landmarks are required. Movement of the camera while getting two successive images is modeled and is compensated.

Thorough analysis of practical issues such as capturing the image from a non‐flat floor, pan/tilt motion of the camera and movement of the camera is presented.

Robot localization in dynamic, cluttered environments is a challenging problem because it is impractical to have enough knowledge to be able to accurately model the robot’s environment in such a manner. This study aims to develop a novel probabilistic method equipped with function approximation techniques which is able to appropriately model the data distribution in Markov localization by using the maximum statistical power, thereby making a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environments.

The parameter vector of the statistical model is in the form of positions of easily detectable artificial landmarks in omnidirectional images. First, using probabilistic principal component analysis, the most likely set of parameters of the environmental model are extracted from the sensor data set consisting of missing values. Next, we use these parameters to approximate a probability density function, using support vector regression that is able to calculate the robot’s pose vector in each state of the Markov localization. At the end, using this density function, a good approximation of conditional density associated with the observation model is made which leads to a sensibly accurate estimation of robot’s pose in extremely dynamic, cluttered indoors environment.

The authors validate their method in an indoor office environment with 34 unique artificial landmarks. Further, they show that the accuracy remains high, even when they significantly increase the dynamics of the environment. They also show that compared to those appearance-based localization methods that rely on image pixels, the proposed localization strategy is superior in terms of accuracy and speed of convergence to a global minima.

By using easily detectable, and rotation, scale invariant artificial landmarks and the maximum statistical power which is provided through the concept of missing data, the authors have succeeded in determining precise pose updates without requiring too many computational resources to analyze the omnidirectional images. In addition, the proposed approach significantly reduces the risk of getting stuck in a local minimum by eliminating the possibility of having similar states.

Landmark‐based navigation of autonomous mobile robots or vehicles has been widely adopted in industry. Such a navigation strategy relies on identification and subsequent recognition of distinctive environment features or objects that are either known a priori or extracted dynamically. This process has inherent difficulties in practice due to sensor noise and environment uncertainty. This paper is to propose a navigation algorithm that simultaneously locates the robots and updates landmarks in a manufacturing environment. A key issue being addressed is how to improve the localization accuracy for mobile robots in a continuous operation, in which the Kalman filter algorithm is adopted to integrate odometry data with scanner data to achieve the required robustness and accuracy. The Kohonen neural networks have been used to recognize landmarks using scanner data in order to initialize and recalibrate the robot position by means of triangulation when necessary.

The purpose of this paper is to present a navigation system designed for highly dynamic environments which is independent from a metrically exact global map.

A navigation system is developed to cope with highly dynamic environments. Here, this refers especially to changes in the environment itself, like the daily deployment or removal of advertisements or special offers in a supermarket. The navigation system is split into a global part, relying on non‐concealable artificial landmarks and a local part containing a behavior‐based control using a dynamic potential field approach. The required information are the definitively static structures and the actual sensor information only.

The system proved to be useful in environments that change frequently and where the presence of many people complicates the perception of landmarks.

The presented navigation system is robust against changes in the environment and provides reliable collision avoidance capabilities.

It is a useful navigation system for autonomous robots dedicated to frequently changing and populated environments.

The purpose of this paper is to present a novel localization scheme using infrared identification (IRID) fused with encoder information.

IRID emitters are mounted on the ceiling in order to divide the floor workspace into sectors. Encoding the IRID signal then allows the mobile robot to identify which sector it is in. The sector information is fused with the dead‐reckoning results for estimation of the robot configuration based on the fact that IRID has highly deterministic characteristics.

Fusing the dead‐reckoning result and the IRID information bounds and in some cases reduces the size of the uncertainty. This enables one to more accurately estimate the robot's configuration.

A new artificial landmark, IRID is developed for mobile robot localization. This paper also demonstrates a framework that fuses the IRID information (deterministic) and dead‐reckoning result (stochastic).

The purpose of this paper is to present a Rao–Blackwellized particle filter (RBPF) approach for the visual simultaneous localization and mapping (SLAM) of small unmanned aerial vehicles (UAVs).

Measurements from inertial measurement unit, barometric altimeter and monocular camera are fused to estimate the state of the vehicle while building a feature map. In this SLAM framework, an extra factorization method is proposed to partition the vehicle model into subspaces as the internal and external states. The internal state is estimated by an extended Kalman filter (EKF). A particle filter is employed for the external state estimation and parallel EKFs are for the map management.

Simulation results indicate that the proposed approach is more stable and accurate than other existing marginalized particle filter-based SLAM algorithms. Experiments are also carried out to verify the effectiveness of this SLAM method by comparing with a referential global positioning system/inertial navigation system.

The main contribution of this paper is the theoretical derivation and experimental application of the Rao–Blackwellized visual SLAM algorithm with vehicle model partition for small UAVs.

The purpose of this paper is to design an integrated localization system for mobile robots in underground environments for exploring and rescuing tasks after incidents and detection of hazard gas in tunnels before ingress.

An integrated localization system mainly based on a strap‐down inertial measurement unit and a digital compass is designed for exploring and rescuing task in coal mines and tunnels. After a system model was founded, a filtering algorithm combining a wavelet‐based pre‐filter with unscented Kalman filters was developed for reckoning tracks of robots and localizing it.

Based on this research, an integrated localization system for robots in underground environments can be developed to explore some regions and rescue people. Although errors of localization exist, performance of the integrated system should be improved if some sensors and landmarks or maps of tunnels are introduced.

What is proposed in this paper is an integrated localization system used in underground environments. In this research, property of environments has been taken into account as an important disturbance when filtering thresholds were set.

The purpose of this paper is to present a localisation system for an indoor rotary‐wing micro aerial vehicle (MAV) that uses three onboard LEDs and base station mounted active vision unit.

A pair of blade mounted cyan LEDs and a tail mounted red LED are used as on‐board landmarks. A base station tracks the landmarks and estimates the pose of the MAV in real time by analysing images taken using an active vision unit. In each image, the ellipse formed by the cyan LEDs is used for 5 degree of freedom (DoF) pose estimation with yaw estimation from the red LED providing the 6th DoF.

About 1‐3.5 per cent localisation error of the MAV at various ranges, rolls and angular speeds less than 45°/s relative to the base station at known location indicates that the MAV can be accurately localised at 9‐12 Hz in an indoor environment.

Line‐of‐sight between the base station and MAV is necessary while limited accuracy is evident in yaw estimation at long distances. Additional yaw sensors and dynamic zoom are among future work.

Provided an unmanned ground vehicle (UGV) as the base station equipped with its own localisation sensor, the developed system encourages the use of autonomous indoor rotary‐wing MAVs in various robotics applications, such as urban search and rescue.

The most significant contribution of this paper is the innovative LED configuration allowing full 6 DoF pose estimation using three LEDs, one camera and no fixed infrastructure. The active vision unit enables a wide range of observable flight as the ellipse generated by the cyan LEDs is recognisable from almost any direction.

Free navigation in indoor environments is one of the main enabling technologies for many service robot applications. Siemens has developed SINAS, a navigation system which currently is primarily targeted towards cleaning robot applications. Its suitability for tough everyday operation has been successfully demonstrated since August 1996 on several occasions, e.g. in several chain store supermarkets. The paper discusses the main requirements of a navigation system for cleaning robots, presents the structure and main features of the SINAS system, and reports experiences and results from the field tests.

The paper aims to propose general design rules and route plan for automated guided vehicle system (AGVS). The AGVS is applied to automated meter verification areas through a case study of meter verification shop floor to verify the feasibility.

The paper gives an appropriate route design for AGVS and proposes an optimized strategy for designed routes and a control system to manage traffic conflict.

This case study indicates that the application of AGVS can highly improve the efficiency of manufacturing and production. Besides, a reasonable transportation plan is beneficial in making the system run smoothly and in cutting conveying time.

The application of AGVS integrates a variety of advanced technologies (i.e. information technology, artificial intelligence, etc.) into the electricity meter verification system, which brings great economic and social benefits via enhancing the verification efficiency and reducing the total labor costs.

The application proposed in the paper solves the problem that the verification almost relies on workers, labor intensity is high and work efficiency is hard to improve. Furthermore, the general rules and strategies of AGVS transportation can be applied not only to the automated electricity meter verification but also in other industrial areas.