Design and Robust Recognition of Omnidirectional Landmarks for Indoor Mobile Robot Positioning

The aim of the work presented in this article is to develop a navigation system that allows a mobile robot to move autonomously in an indoor environment using perceptions of multiple events. A topological navigation system based on events that imitates human navigation using sensorimotor abilities and sensorial events is presented. The increasing interest in building autonomous mobile systems makes the detection and recognition of perceptions a crucial task. The system proposed can be considered a perceptive navigation system as the navigation process is based on perception and recognition of natural and artificial landmarks, among others. The innovation of this work resides in the use of an integration interface to handle multiple events concurrently, leading to a more complete and advanced navigation system. The developed architecture enhances the integration of new elements due to its modularity and the decoupling between modules. Finally, experiments have been carried out in several mobile robots, and their results show the feasibility of the navigation system proposed and the effectiveness of the sensorial data integration managed as events.

Generally, the mobile robots are researched and used in many applications. The fundamental method of navigation is localization, which includes aspects such as sensing, mapping, localization, planning and control. In order to we build the navigation system for mobile robot operating in unmodified environments, we must know the mobile robot’s position in reference frame. Typically reference frame is a Cartesian or we can specify the landmark as reference position. The dead reckoning based on odometry is widely to identify and classify landmarks. Dead reckoning systems estimate the position of the mobile robot with respect to an initial point by measuring linear and angular velocities or accelerations. There are also hybrid models, which have sensor systems of both dead reckoning and landmark recognition. Dead reckoning is the estimate of current position based on a previously determined position or estimated speeds from a known past position over time. This can also determine the future position by projecting an ordered course and speed of advance from a known present position. A disadvantage of dead reckoning is that new positions are calculated solely from previous positions. The errors of the process are cumulative, so the error in the position fix grows with time (Hans-Joachim von der Hardt & Didier Wolf Rene Husson, 1996; Hye Ri Park et al., 2009). Odometry is widely used in mobile robots to estimate their position relative to reference frame. This method estimates the position over time with errors accumulation due to the integration of velocity measurements (Cristina Tar in Sauer and Hermann Brugger, 2001; Houshangi, N. and Azizi, F., 2006; Agostino Martinelli, 2002). The navigation system for mobile robot based on odometry has typically problem with inaccuracy. Because of the odometry problems navigation systems for mobile robot application cannot work solely on dead reckoning. This is the reason that the navigation system needs the addition of sensory information. Additional sensors can be defined as information provided to assist the accomplishment of a task by a system. Practically, sensor fusion refers to the integration process where the different sources of sensory information are combined into one representational format. Typically in navigation systems sensory information can be fused from complementary sensors, redundant sensors or even from a single sensor over a period of time. The advantage of sensory fusion provides uncertainty and noise reduction, failure toleration and extended flexibility of sensor ability.

Accurate and reliable localization is a base requirement for a mobile robot. Landmark localization is a very common technique to do this. The arrangement of landmarks is important for reliable and accurate estimation of localization data. In this paper the optimized arrangement of artificial landmarks is discussed and a solution for building an accurate and reliable localization system based on combining artificial and natural landmarks is presented. Landmark placement is done by definition and optimization of a confidence level describing the expected localization estimation error. Areas in the environment which are ill conditioned by natural landmarks are furnished with additional artificial landmarks. The proposed system has been tested in a simulation and with a mobile robot in a real office environment.

A novel approach for landmark recognition based on the perception, reasoning, action, and expectation (PREACTE) paradigm is presented for the navigation of autonomous mobile robots. PREACTE uses expectations to predict the appearance and disappearance of objects, thereby reducing computational complexity and locational uncertainty. It uses an innovative concept called dynamic model matching (DMM), which is based on the automatic generation of landmark description at different ranges and aspect angles and uses explicit knowledge about maps and landmarks. Map information is used to generate an expected site model (ESM) for search delimitation, given the location and velocity of the mobile robot. The landmark recognition vision system generates 2-D and 3-D scene models from the observed scene. The ESM hypotheses are verified by matching them to the image model. Experimental results that verify the performance of the PREACTE and DMM algorithms for real imagery are also presented.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

The obstacle avoidance and navigation are important tasks for a mobile robot in applications such as industry, military, exploration and automated vehicles. This paper presents an implementation of mapping and navigation of autonomous mobile robot using Robot Operating System (ROS) and Orbbec Astra camera. In recent days, Lidar is used for the mapping and navigation problem by sensing the objects which are above floor level by using only a single horizontal scanning line and this may result in inaccurate generation of map leads to collision during autonomous navigation of mobile robots. In order to overcome limitation of Lidar, Orbbec Astra (Kinect) camera is used for mapping of indoor environment which can detect objects which are above and below the floor level and navigation of mobile robot from start to target location without colliding with obstacles. Experimental results show the map of the indoor environment generated using Orbbec Astra camera is matched with real indoor environment, and generated map is tested experimentally to navigate the mobile robot from start to target location without collision with obstacles.KeywordsMobile robotsMappingNavigationOrbbec Astra cameraROS

A mobile robot is a vehicle that navigates autonomously through an unknown or partially known environment. Research in the field of mobile robots has received considerable attention in the past decade due to its wide range of potential applications, from surveillance to planetary exploration, and the research opportunities it provides, including virtually the whole spectrum of robotics research from vehicle control to symbolic planning (see for example [Har88b] for an analysis of the research issues in mobile robots). In this paper we present our investigation of some the issues in one of the components of mobile robots: perception. The role of perception in mobile robots is to transform data from sensors into representations that can be used by the decision-making components of the system. The simplest example is the detection of potentially dangerous regions in the environment (i. e. obstacles) that can be used by a path planner whose role is to generate safe trajectories for the vehicle. An example of a more complex situation is a mission that requires the recognition of specific landmarks, in which case the perception components must produce complex descriptions of the sensed environment and relate them to stored models of the landmarks.KeywordsMobile RobotLocus AlgorithmRange ImageAutonomous VehicleRange SensorThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

This work describes the implementation of some of the neural systems that will enable a mobile robot to actively explore and learn its environment visually. These systems perform the real-time extraction of robust visual features, the segmentation of landmarks from the background and from each other using binocular attentional mechanisms, the predictive binocular tracking of landmarks, and the learning and recognition of landmarks from their features. Also described are preliminary results of incorporating most of these systems into a mobile robot called MAVIN, which can demonstrate the visual exploration of simplified landmarks. Finally, we discuss plans for using similar neural strategies to extend MAVIN's capabilities by implementing a biologically plausible system for navigating through an environment that has been learned by exploration. This explorational learning consists of quantizing the environment into orientation-specific place fields generated by the view-based spatial distribution of landmarks, and associating these place fields in order to form qualitative, behavioral, spatial maps.

Numerous indoor positioning technologies and systems have been proposed to localize people and objects in large buildings. Wi-Fi and Bluetooth positioning systems using fingerprinting have gained popularity, due to the wide availability of existing infrastructure. Unfortunately, the implementation of fingerprinting-based methods requires time-consuming radio surveys to prepare databases (RSSI maps) that serve as a reference for the radio signal. These surveys must be conducted for each individual building. Here, we investigate the possibility of using simulated RSSI maps with fingerprinting-based indoor localization systems. We discuss the suitability of the two popular radio wave propagation models for the preparation of RSSI reference data: ray tracing and multiwall. Based on an analysis of several representative indoor scenarios, we evaluated the performance of RSSI distribution maps obtained from simulations versus maps obtained from measurement campaigns. An experimental positioning system developed by the authors was used in the study. Based on Bluetooth Low Energy beacons and mobile devices (smartphones), the system uses fingerprinting followed by a particle filter algorithm to estimate the user’s current position from RSSI measurements and a reference spatial RSSI distribution database for each Bluetooth beacon in the building. The novelty of our contribution is that we evaluate the performance of the positioning system with RSSI maps prepared both from measurements and using the two most representative indoor propagation methods, in three different environments in terms of structure and size. We compared not only the three RSSI maps, but also how they influence the performance of the fingerprint-based positioning algorithm. Our original findings have important implications for the development of indoor localization systems and may reduce deployment times by replacing reference measurements with computer simulations. Replacing the labor-intensive and time-consuming process of building reference maps with computer modeling may significantly increase their usefulness and ease of adaptation in real indoor environments.

&lt;p&gt;Nowadays, the development of computer vision technology help to overcome track and identify humans within a location in the complex environment through mobile robots, which gives the motivation to presents a vision-based approach to a mobile security robot. The proposed system utilizes a wireless camera to detect the objects in the field of robot view. Principle component analysis (PCA) algorithm and filters are used to implement and demonstrate the process of the images. This gives the designed system the ability to recognize objects independently from current light conditions. Frame tracking in the images uses an attention system to get an estimate of the position of a person. This estimate helps the applied camera to identify objects with changing background lighting conditions such as a fire inside a building. By using this estimate, the applied camera could identify objects with changing background lighting conditions such as a fire inside premises. The system has been tested using the MATLAB environment, and the empirical performance explains the efficiency and strongness of the suggested device.&lt;/p&gt;

Scene understanding is widely linked to the perception of it. Lane detection and tracking are commonly used in the context of autonomous transportation to estimate the drivable area on marked road. Real-time, accurate and efficient analyses are particularly critical when mobile robots are considered. Moreover, safe trajectories are of importance for those robotic vehicles evolving other vehicles or humans interactions. This paper proposes an unsupervised and robust vision-based approach that provides Lane Marking Detection (LMD) to help the positioning system. The approach is applied in two different environments of AKKA Research projects: Link & Go, an autonomous car; and AIR-COBOT, a collaborative mobile robot which inspects aircrafts during maintenance operations. The autonomous car has to travel on roads and to stay in its ego-lane. The mobile robot cooperates with human and has to circulate in the airport and in hangars. In both contexts, painted lanes on the ground could help the navigation system. We demonstrate the benefits of our proposal through an evaluation of the proposed approach on real datasets with appropriate metrics.

This paper investigates the applicability of a developed Magnetic Positioning System (MPS) as a support for a dead-reckoning inertial navigation system (DR-INS) for pedestrian applications. The integrated system combines the complementary properties of the separate systems, operating over long periods of time and in cluttered indoor areas with partial nonline-of-sight conditions. The obtained results show that the proposed approach can effectively improve the coverage area of the MPS and the operation time with bounded errors of the DR-INS. In particular, a solution that provides bounded position errors of 1–2 m over significantly long periods of time up to 45 min, in realistic indoor environments, is demonstrated. Moreover, system applicability is also shown in those scenarios where arbitrary orientations of the MPS mobile node are considered and an MPS position estimate is not available due to less than three distance measurements.

This paper presents a projection-based location system in an indoor wireless local area network (WLAN) environment. Our algorithm projects the received signal strength (RSS) onto a discriminative space such that the information of all access points (APs) is more efficiently utilized. The projection is determined by multiple discriminant analysis (MDA), thereby guaranteeing maximal discriminative information involved in the positioning system. The study conducts a series of experiments on the effects of our approach in a realistic indoor environment. The results show that not only is the positioning accuracy significantly improved, but the system cost, including the computation and data collection, is also greatly reduced at the same time. This is because our approach extracts only useful information for positioning, whereas the redundant noise is discarded to avoid the problem of overfitting and unnecessary calculations. Compared with prior works, this technique can produce a more graceful balance between the positioning accuracy and the computational complexity for the resource-weak clients.

Currently, the application of control systems has been applied in various scientific fields including mechatronics and robotics. Applications in the branch of robotics are also growing day by day not only with conventional controls but also with intelligent systems. An autonomous robot in carrying out certain missions in an unknown environment requires information about the location itself and the environment through the map. A process to identify a position without a map is called a localization function on the robot. Mobile robots building maps and localization are two fundamental tasks when mobile robots work in indoor environments. With 2D laser scanning (LiDAR) data obtained in real-time, the robot can calculate the area of ​​all empty spaces in a room, then can choose the center of the room as its position for map building. The objective of this research is to implement a two-dimensional mapping method using LiDAR. The algorithm used in this study is the Gmapping Technique on ROS. The main purpose of this research is to map mobile robots with LIDAR sensors using the Robot Operating System for navigation and positioning of mobile robots. Through the actual experimental results, the mobile robot will move with a 2-dimensional mapping process.

Text input using hand gestures is an essential component of human–computer interaction technology, providing users with a more natural and enriching interaction experience. Nevertheless, the current gesture input methods have a variety of issues, including a high learning cost for users, poor input performance, and reliance on hardware. To solve these problems and better meet the interaction requirements, a hand recognition-based text input method called iHand is proposed in this paper. In iHand, a two-branch hand recognition algorithm combining a landmark model and a lightweight convolutional neural network is used. The landmark model is used as the backbone network to extract hand landmarks, and then an optimized classification head, which can preserve the space structure of landmarks, is designed to classify gestures. When the landmark model fails to extract hand landmarks, a lightweight convolutional neural network is employed for classification. Regarding the way letters are entered, to reduce the learning cost, the sequence of letters is mapped as a two-dimensional layout, and users can type with seven simple hand gestures. Experimental results on the public datasets show that the proposed hand recognition algorithm achieves high robustness compared to state-of-the-art approaches. Furthermore, we tested the performance of users’ initial use of iHand for text input. The results showed that the iHand’s average input speed was 5.6 words per minute, with the average input error rate was only 1.79%.

Aiming at the positioning and navigation system of indoor mobile robot, a fuzzy control method of indoor location navigation based on UWB communication technology is proposed. Firstly, based on the UWB radio signal, the mobile robot localization information is obtained by the twice triangular centroid localization algorithm, and the reactive fuzzy controller is used to realize the local path planning. An improved contour following method is proposed to make the mobile robot get out of the trap area quickly and reach the target. This method can effectively drive the mobile robot to reach the target in a complex and unknown environment with reasonable path. Finally, a fourwheel indoor mobile robot is taken as an example in the indoor environment of 10m × 10m. The experiment proves that the method can effectively drive the mobile robot to reach the target point in the unknown environment with the better path.