Optimized landmark arrangement for absolute localization-a practical approach

Owing to the presence of multiple obstacles or changes in the positions of surroundings objects, the localization of a mobile robot using only natural landmarks is difficult in indoor environments. Consequently, in these environments, the use of artificial landmarks to achieve robust localization is necessary. To this end, we propose a global localization method for mobile robots, using reflectors as indistinguishable artificial landmarks, not the manmade signs have a certain pattern. In previous studies, it has been shown that localization is possible using information about the distance between indistinguishable artificial landmarks. We applied this principle in a real-world situation with many obstacles or in a dynamic environment and confirmed that robust, high-accuracy global localization is possible.

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.

Deformable models have been intensively studied in image analysis through the last decade, and are used for detection and recognition of flexible or rigid templates under diverse viewing conditions. Genetic algorithm (GA) based deformable models are used for generic visual landmark detection and interpretation. The developed system allows topologic localization and navigation using natural and artificial landmarks, exploiting deformable models' ability for handling landmark perspective variations. The resulting perception module has been integrated successfully in a complex navigation system. Various experimental results in real environments are presented on this paper, showing the effectiveness and capacity of the landmark detection and reading system.

In this paper, we present an omnidirectional artificial landmark model and a robust artificial landmark recognition algorithm for indoor mobile robot positioning. The landmark model encodes identities with nested circles in black and white, which provides stable edge response and enables strong tolerance to various lighting conditions and perspective distortions. The corresponding positioning system uses a single upward-facing webcam as the vision sensor to capture landmarks. To address the effect of the lighting and sensing noise, the topological contour analysis is applied to detect landmarks, and the dynamic illumination adjustment is used to assist landmark recognition. Based on the landmark recognition, the absolute position of the camera in the environment is estimated using a trilateration algorithm. The landmark model and positioning system are tested with a mobile robot in a real indoor environment. The results show that the purposed technique provides autonomous indoor positioning for mobile robots with high robustness and consistency.

Simultaneous Localization and Mapping (SLAM) is the core issue in the field of mobile robots. Laser SLAM is one of the widely used solutions for the application of SLAM in engineering. Laser SLAM estimates the change of the robot pose using the features of the environment. However, it is difficult to extract natural features in a highly similar environment. Therefore, artificial landmarks are needed to be placed in the environment when necessary. In this paper, the reflective characteristics of the artificial landmark are analyzed experimentally, and two feature parameters for the extraction of the artificial landmark are proposed based on their reflective characteristics. One of them is the intensity of the reflected light received by the laser ranger, and the other is the number of the laser points that the artificial landmark can continuously reflect back in each scan. Through the statistical analysis of the laser data collected in the experiment, the specific values of these two parameters are determined, and the detection range of the laser ranger to effectively extract the landmark under the selected parameters is measured experimentally. These results provide experimental support for the deployment of the landmarks in the environment.

The aim of this paper is to explore the possibility of using geo-referenced aerial images to augment an Unmanned Aerial Vehicle (UAV) navigation system in case of GPS failure. The proposed method is based on image matching between the current view from a monocular video camera and a previously known database of geo-referenced images. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or artificial landmarks. The novel idea is to perform global localization, position tracking, localization failure recovery and safe landing. Key points of current video frame and reference database are extracted by implementing Speeded-Up Robust Features (SURF) algorithm on a NVIDIA Geforce 240M graphics processing unit (GPU). The proposed system has been tested on real flight data and testing results confirmed that the proposed system is real-time and sufficiently robust to scale, rotation, illumination, noise and affine projection, thus it can be an ideal scheme for autonomous navigation system for UAVs under GPS blockage conditions.

Recently, research on mobile robotics has been focused on achieving reliable performance on autonomous systems. We believe that one possible way to do this is by using landmarks to bound uncertainty during the path planning and navigation stages. In this paper, we present an algorithm to compute the position of artificial visual landmarks in a mobile robot workspace.We aim to maximize the region in the workspace from where a landmark can be seen, i.e., to define the position of the landmarks in the workspace. After pointing out that this problem is combinatorial in nature, we propose a simulated annealing type of technique to find the optimal landmark arrangement.

This paper proposes a pose-graph based SLAM method using an upward-looking camera and artificial landmarks for AGVs in factory environments. The proposed method provides a way to acquire the camera extrinsic matrix and improves the accuracy of feature observation using a low-cost camera. SLAM is conducted by optimizing AGV`s explored path using the artificial landmarks installed on the ceiling at various locations. As the AGV explores, the pose nodes are added based on the certain distance from odometry and the landmark nodes are registered when AGV recognizes the fiducial marks. As a result of the proposed scheme, a graph network is created and optimized through a G2O optimization tool so that the accumulated error due to the slip is minimized. The experiment shows that the proposed method is robust for SLAM in real factory environments.

Robot localization is one of the hottest research spots. Many scholars have studied the landmark localization which is cheap, stable, and simple in the indoor environment. This paper presents a low-cost localization system based on the artificial landmarks with two degree of freedom platform camera. The landmarks are put on the ceiling in order to decrease the interference. A 2-DOF pan-tilt has been used to help the camera track landmarks so that the landmark velocity in the picture is low enough to avoid that the robot can't recognize landmarks because of the high speed. Using the pan-tilt can also expand the recognition range and decrease the number of landmarks. The experiments have shown that the robot can recognize landmarks well when it moves fast by using the 2-DOF pan-tilt, besides, the localization works very well.

Presents a new approach to accurate and reliable dead-reckoning with mobile robots. The approach makes use of special properties of the author's multi-degree-of-freedom (MDOF) mobile platform, in which two differential-drive mobile robots (called trucks) are physically connected through a compliant linkage. Using one linear and two rotary encoders, the system can measure the relative distance and bearing between the two trucks. During operation, both trucks perform conventional dead-reckoning with their wheel encoders, but, in addition, use information about their relative position to correct dead-reckoning errors. The author's system, called Compliant Linkage Autonomous Platform with Position Error Recovery (CLAPPER), requires neither external references (such as navigation beacons, artificial landmarks, known floorplans, or satellite signals), nor inertial navigation aids (such as accelerometers or gyros). Nonetheless, the experimental results included in this paper show one to two orders of magnitude better positioning accuracy than systems based on conventional dead-reckoning. >

This paper presents a novel approach for mobile robot localization using monocular vision. The proposed approach locates a robot relative to the target to which the robot moves. Two points are selected from the target as two featurepoints. Once the coordinates in an image of the two feature points are detected, the position and motion direction of the robot can be determined according to the detected coordinates. Unlike those reported geometry pose estimation orlandmarks matching methods, this approach requires neither artificial landmarks nor an accurate map of indoor environment. It needs less computation and can simplify greatly the localization problem. The validity and flexibility of the proposed approach is demonstrated by experiments performed on real images. The results show that this new approach is not only simple and flexible but also has high localization precision.Keywords: mobile robot, localization, monocular vision, single image, feature points 1. INTRODUCTION

Perceiving the position of the mobile robot in an environment is an important element for an autonomous or semi-autonomous robot. In this paper we present a novel localization paradigm using topological map with tiered features, which is robust to rotation, illumination variations and scale changes. The topological model is extracted from natural landmarks and the normalized Zernike moments are calculated as main landmark features. By color matching and Zernike moments matching based on a probabilistic voting method between input scene and image models, natural landmarks can be recognized. The result is then fused with sonar data. The technique is especially adapted to indoor environment. Experimental results validate the favorable performance of this approach

Unmanned Aircraft Systems (UAS, commonly known as aerial drones) are widely used for remote inspection and evaluation in Structural Health Monitoring (SHM) applications. While in most cases the drone is only equipped with a camera and collects images and videos, there is a high interest for structural evaluation that goes beyond visual inspection and provides information on the internal structure. For the latter, specialized sensors need to be used which in many cases need to be attached to the structure. This work presents a low-cost autonomous UAS for attaching and retrieving sensor packages to the underside of structures for SHM applications. Such a system can be used for nondestructive evaluation of structures by deploying sensors like accelerometers on bridges or tunnels. The proposed system is experimentally evaluated and is shown to be capable of the task utilizing only onboard systems and an artificial landmark with an operator to change between deployment and retrieval missions.

Peer-to-peer (P2P) paradigm is a promising way to provide services to the masses for a very low cost, and hence, P2P services have been gaining significant attention. Since P2P services usually operate over their users' resources, without using central servers, their performance is becoming of interest to researchers. Several existing solutions rely on supernodes, called landmarks, to enhance system performance. The landmarks are placed manually based on the density and the distribution of the nodes in the corresponding P2P network, and landmark locations are presumed to be determined before system setup. However, this assumption is not realistic since, in a P2P system, no global view about the peers' locations exists. Hence, the best landmark locations would not be known a priori. We propose a family of distributed landmark placement algorithms, called DLP, to address this issue. We implement four versions of the DLP family and evaluate their landmark placements in a simulation environment. We define two performance metrics to assess their performance as compared to manually placed landmarks. Our results show that the DLP algorithms can generate landmark locations that are on par with the manual placement and significantly reduce the landmark-to-peer latencies.

A precise localization system and a map that properly represents the environment are fundamental for several robotic applications. Traditional LiDAR SLAM algorithms are particularly susceptible to underestimating the distance covered by real robots in environments with few geometric features. Common industrial confined spaces, such as ducts and galleries, have long and homogeneous structures, which are difficult to map. In this paper, we propose a novel approach, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKF-LOAM</i> , which fuses wheel odometry and IMU (Inertial Measurement Unit) data into the LeGO-LOAM algorithm using an Extended Kalman Filter. For that, the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKF-LOAM</i> uses a simple and lightweight adaptive covariance matrix based on the number of detected geometric features. Simulated and real-world experiments with the EspeleoRobô, a service robot designed to inspect confined places, show that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">EKF-LOAM</i> method reduces the underestimating problem, with improvements greater than 50% when compared to the original LeGO-LOAM algorithm. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the challenges of autonomous navigation for mobile ground robots within confined and unstructured environments. Here, we propose a data fusion framework that uses common sensors (such as LiDARs, wheel odometry, and inertial devices) to improve the simultaneous localization and mapping (SLAM) capabilities of a robot without GPS and compass. This approach does not need artificial landmarks nor ideal light and, in scenarios with few geometric features, increases the performance of LiDAR SLAM techniques based on edge and planar features. We also provide a robust controller for the autonomous navigation of the robot during the mapping of a tunnel. Experiments carried out in simulation and real-world confined places show the effectiveness of our approach. In future work, we shall incorporate other sensors, such as cameras, to improve the SLAM process.