Mobile Robot Self-localization Research Articles

SLAMICP Library: Accelerating Obstacle Detection in Mobile Robot Navigation via Outlier Monitoring following ICP Localization.

The Iterative Closest Point (ICP) is a matching technique used to determine the transformation matrix that best minimizes the distance between two point clouds. Although mostly used for 2D and 3D surface reconstruction, this technique is also widely used for mobile robot self-localization by means of matching partial information provided by an onboard LIDAR scanner with a known map of the facility. Once the estimated position of the robot is obtained, the scans gathered by the LIDAR can be analyzed to locate possible obstacles obstructing the planned trajectory of the mobile robot. This work proposes to speed up the obstacle detection process by directly monitoring outliers (discrepant points between the LIDAR scans and the full map) spotted after ICP matching instead of spending time performing an isolated task to re-analyze the LIDAR scans to detect those discrepancies. In this work, a computationally optimized ICP implementation has been adapted to return the list of outliers along with other matching metrics, computed in an optimal way by taking advantage of the parameters already calculated in order to perform the ICP matching. The evaluation of this adapted ICP implementation in a real mobile robot application has shown that the time required to perform self-localization and obstacle detection has been reduced by 36.7% when obstacle detection is performed simultaneously with the ICP matching instead of implementing a redundant procedure for obstacle detection. The adapted ICP implementation is provided in the SLAMICP library.

A Procedure for Taking a Remotely Controlled Elevator with an Autonomous Mobile Robot Based on 2D LIDAR

Navigating between the different floors of a multistory building is a task that requires walking up or down stairs or taking an elevator or lift. This work proposes a procedure to take a remotely controlled elevator with an autonomous mobile robot based on 2D LIDAR. The application of the procedure requires ICP matching for mobile robot self-localization, a building with remotely controlled elevators, and a 2D map of the floors of the building detailing the position of the elevators. The results show that the application of the procedure enables an autonomous mobile robot to take a remotely controlled elevator and to navigate between floors based on 2D LIDAR information.

Self-Localization of Mobile Robot Based on Beacon Beam of TOF Laser Sensor Mounted on Pan-Tilt Actuator: Estimation Method that Combines Spot Coordinates on Laser Receiver and Odometry

We propose a method for estimating the position and attitude of a mobile robot using a laser beam. To this end, we employ the real-world clicker, which was developed by the authors and consisted of a system that uses a TOF (time of flight) laser sensor mounted on a pan-tilt actuator to measure the three-dimensional position of the laser spot. A laser receiver was mounted on top of the mobile robot, and the position of the laser spot was measured by both the camera installed below the receiver and real-world clicker. We propose an algorithm that allows the estimation of the robot’s self-position and attitude while the robot is in motion by combining the use of odometry, and validate it through simulations and experiments.

Self-Localization of Mobile Robots Using a Single Catadioptric Camera with Line Feature Extraction.

This paper presents a novel self-localization technique for mobile robots using a central catadioptric camera. A unified sphere model for the image projection is derived by the catadioptric camera calibration. The geometric property of the camera projection model is utilized to obtain the intersections of the vertical lines and ground plane in the scene. Different from the conventional stereo vision techniques, the feature points are projected onto a known planar surface, and the plane equation is used for depth computation. The 3D coordinates of the base points on the ground are calculated using the consecutive image frames. The derivation of motion trajectory is then carried out based on the computation of rotation and translation between the robot positions. We develop an algorithm for feature correspondence matching based on the invariability of the structure in the 3D space. The experimental results obtained using the real scene images have demonstrated the feasibility of the proposed method for mobile robot localization applications.

Mobile Robot Self-Localization Using Omnidirectional Vision with Feature Matching from Real and Virtual Spaces

This paper presents a novel self-localization technique for mobile robots based on image feature matching from omnidirectional vision. The proposed method first constructs a virtual space with synthetic omnidirectional imaging to simulate a mobile robot equipped with an omnidirectional vision system in the real world. In the virtual space, a number of vertical and horizontal lines are generated according to the structure of the environment. They are imaged by the virtual omnidirectional camera using the catadioptric projection model. The omnidirectional images derived from the virtual and real environments are then used to match the synthetic lines and real scene edges. Finally, the pose and trajectory of the mobile robot in the real world are estimated by the efficient perspective-n-point (EPnP) algorithm based on the line feature matching. In our experiments, the effectiveness of the proposed self-localization technique was validated by the navigation of a mobile robot in a real world environment.

Self-Localization of Mobile Robot Using Map Matching Algorithm

Self-Localization of Mobile Robot Using Map Matching Algorithm

Mobile Robot Self-Localization with 2D Push-Broom LIDAR in a 2D Map.

This paper proposes mobile robot self-localization based on an onboard 2D push-broom (or tilted-down) LIDAR using a reference 2D map previously obtained with a 2D horizontal LIDAR. The hypothesis of this paper is that a 2D reference map created with a 2D horizontal LIDAR mounted on a mobile robot or in another mobile device can be used by another mobile robot to locate its location using the same 2D LIDAR tilted-down. The motivation to tilt-down a 2D LIDAR is the direct detection of holes or small objects placed on the ground that remain undetected for a fixed horizontal 2D LIDAR. The experimental evaluation of this hypothesis has demonstrated that self-localization with a 2D push-broom LIDAR is possible by detecting and deleting the ground and ceiling points from the scan data, and projecting the remaining scan points in the horizontal plane of the 2D reference map before applying a 2D self-location algorithm. Therefore, an onboard 2D push-broom LIDAR offers self-location and accurate ground supervision without requiring an additional motorized device to change the tilt of the LIDAR in order to get these two combined characteristics in a mobile robot.

Particle Smoother-Based Landmark Mapping for the SLAM Method of an Indoor Mobile Robot with a Non-Gaussian Detection Model

HF-band radio-frequency identification (RFID) is a robust identification system that is rarely influenced by objects in the robot activity area or by illumination conditions. An HF-band RFID system is capable of facilitating a reasonably accurate and robust self-localization of indoor mobile robots. An RFID-based self-localization system for an indoor mobile robot requires prior knowledge of the map which contains the ID information and positions of the RFID tags used in the environment. Generally, the map of RFID tags is manually built. To reduce labor costs, the simultaneous localization and mapping (SLAM) technique is designed to localize the mobile robot and build a map of the RFID tags simultaneously. In this study, multiple HF-band RFID readers are installed on the bottom of an omnidirectional mobile robot and RFID tags are spread on the floor. Because the tag detection process of the HF-band RFID system does not follow a standard Gaussian distribution, extended Kalman filter- (EKF-) based landmark updates are unsuitable. This paper proposes a novel SLAM method for the indoor mobile robot with a non-Gaussian detection model, by using the particle smoother for the landmark mapping and particle filter for the self-localization of the mobile robot. The proposed SLAM method is evaluated through experiments with the HF-band RFID system which has the non-Gaussian detection model. Furthermore, the proposed SLAM method is also evaluated by a range and bearing sensor which has the standard Gaussian detection model. In particular, the proposed method is compared against two other SLAM methods: FastSLAM and SLAM methods utilize particle filter for both the landmark updating and robot self-localization. The experimental results show the validity and superiority of the proposed SLAM method.

Mobile Robot Self-localization Based on Multi-sensor Fusion Using Limited Memory Kalman Filter with Exponential Fading Factor

Mobile Robot Self-localization Based on Multi-sensor Fusion Using Limited Memory Kalman Filter with Exponential Fading Factor

Mobile Robot Self-localization Based on Multi-sensor Fusion Using Limited Memory Kalman Filter with Exponential Fading Factor

Mobile Robot Self-localization Based on Multi-sensor Fusion Using Limited Memory Kalman Filter with Exponential Fading Factor

Indoor mobile robot self-localization based on a low-cost light system with a novel emitter arrangement

This paper proposes a low-cost infrared (IR) LED system with a novel arrangement that enables the self-localization of an indoor mobile robot. The proposed system uses only low-cost IR LED emitters installed on the ceiling of a building and low-cost IR receivers, each equipped with a photodiode, located on top of the mobile robot for detection. The IR LEDs, which are driven by an on-off keying modulator with various frequencies, are used as active landmarks. The mobile robot localizes itself based on the IR LED signals it receives. Although it would be desirable to assign a unique ID to each emitter, this would be difficult to realize because of the limited number of available frequencies and the production cost resulting from the use of independent microcontrollers. Therefore, we designed a novel landmark configuration consisting of an IR LED array with unique ID-encoding capabilities based on a combination of different frequencies and the repeated use of each ID encoding LED to address the problem of limited frequencies. The novel landmark arrangement reduces the costs associated with production, ID encoding, and computation. A Monte-Carlo localization method with a belief function is utilized to estimate the position and orientation of the mobile robot based on the decoded ID information and received signal strength. The proposed system was tested in a real environment and the experimental results confirmed the validity and accuracy of the system and method.

Map-Matching Algorithms for Robot Self-Localization: A Comparison Between Perfect Match, Iterative Closest Point and Normal Distributions Transform

The self-localization of mobile robots in the environment is one of the most fundamental problems in the robotics navigation field. It is a complex and challenging problem due to the high requirements of autonomous mobile vehicles, particularly with regard to the algorithms accuracy, robustness and computational efficiency. In this paper, we present a comparison of three of the most used map-matching algorithms applied in localization based on natural landmarks: our implementation of the Perfect Match (PM) and the Point Cloud Library (PCL) implementation of the Iterative Closest Point (ICP) and the Normal Distribution Transform (NDT). For the purpose of this comparison we have considered a set of representative metrics, such as pose estimation accuracy, computational efficiency, convergence speed, maximum admissible initialization error and robustness to the presence of outliers in the robots sensors data. The test results were retrieved using our ROS natural landmark public dataset, containing several tests with simulated and real sensor data. The performance and robustness of the Perfect Match is highlighted throughout this article and is of paramount importance for real-time embedded systems with limited computing power that require accurate pose estimation and fast reaction times for high speed navigation. Moreover, we added to PCL a new algorithm for performing correspondence estimation using lookup tables that was inspired by the PM approach to solve this problem. This new method for computing the closest map point to a given sensor reading proved to be 40 to 60 times faster than the existing k-d tree approach in PCL and allowed the Iterative Closest Point algorithm to perform point cloud registration 5 to 9 times faster.

全方位カメラと発光型ランドマークによる自律型移動ロボットの自己位置検出

全方位カメラと発光型ランドマークによる自律型移動ロボットの自己位置検出

Design and recognition of artificial landmarks for reliable indoor self-localization of mobile robots

This article presents a self-localization scheme for indoor mobile robot navigation based on reliable design and recognition of artificial visual landmarks. Each landmark is patterned with a set of concentric circular rings in black and white, which reliably encodes the landmark’s identity under environmental illumination. A mobile robot in navigation uses an onboard camera to capture landmarks in the environment. The landmarks in an image are detected and identified using a bilayer recognition algorithm: A global recognition process initially extracts candidate landmark regions across the whole image and tries to identify enough landmarks; if necessary, a local recognition process locally enhances those unidentified regions of interest influenced by illumination and incompleteness and reidentifies them. The recognized landmarks are used to estimate the position and orientation of the onboard camera in the environment, based on the geometric relationship between the image and environmental frames. The experiments carried out in a real indoor environment show high robustness of the proposed landmark design and recognition scheme to the illumination condition, which leads to reliable and accurate mobile robot localization.

2 次元および 3 次元点群からの人間の環境認識のシミュレータによる調査と実機による自己位置推定との比較

When given a map, a human can select an appropriate route and reach a destination. In this research, we focused on human smart ability of self-localization. Recently point cloud information acquired by 2D or 3D laser range finder (LRF) is often used for mobile robot self-localization. In this research, human self-localization ability when given point cloud information (not RGB image information) is investigated by questionnaire using a developed computer simulator. The results show that 3D information is more effective than 2D information especially in case of given only sparse points. It is because the road surface information can be obtained from 3D point cloud. An experiment in which a developed mobile robot equipped with 2D and 3D LRF travels a given outdoor course, shows the superiority of 3D point cloud information to 2D one.

Mobile robot self-localization in unstructured environments based on observation localizability estimation with low-cost laser range-finder and RGB-D sensors

When service robots work in human environments, unexpected and unknown moving people may deteriorate the convergence of robot localization or even cause failure localization if the environment is crowded. In this article, a multisensor observation localizability estimation method is proposed and implemented for supporting reliable robot localization in unstructured environments with low-cost sensors. The contribution of the approach is a strategy that combines noisy laser range-finder data and RGB-D data for estimating the dynamic localizability matrix in a probabilistic framework. By aligning two sensor frames, the unreliable part of the laser readings that hits unexpected moving people is fast extracted according to the output of a RGB-D-based human detector, so that the influence of unexpected moving people on laser observations can be explicitly factored out. The method is easy for implementation and is highly desirable to ensure robustness and real-time performance for long-term operation in populated environments. Comparative experiments are conducted and the results confirm the effectiveness and reliability of the proposed method in improving the localization accuracy and reliability in dynamic environments.

Development of Autonomous Mobile Robot “MML-05” Based on i-Cart Mini for Tsukuba Challenge 2015

[abstFig src='/00280004/04.jpg' width='300' text='Autonomous mobile robots entered in the Tsukuba Challenge 2015' ] This paper describes a self-localization method for autonomous mobile robots entered in the Tsukuba Challenge 2015. One of the important issues in autonomous mobile robots is accurately estimating self-localization. An occupancy grid map, created manually before self-localization has typically been utilized to estimate the self-localization of autonomous mobile robots. However, it is difficult to create an accurate map of complex courses. We created an occupancy grid map combining local grid maps built using a leaser range finder (LRF) and wheel odometry. In addition, the self-localization of a mobile robot was calculated by integrating self-localization estimated by a map and matching it to wheel odometry information. The experimental results in the final run of the Tsukuba Challenge 2015 showed that the mobile robot traveled autonomously until the 600 m point of the course, where the occupancy grid map ended.

Design of an HF-Band RFID System with Multiple Readers and Passive Tags for Indoor Mobile Robot Self-Localization.

Radio frequency identification (RFID) technology has already been explored for efficient self-localization of indoor mobile robots. A mobile robot equipped with RFID readers detects passive RFID tags installed on the floor in order to locate itself. The Monte-Carlo localization (MCL) method enables the localization of a mobile robot equipped with an RFID system with reasonable accuracy, sufficient robustness and low computational cost. The arrangements of RFID readers and tags and the size of antennas are important design parameters for realizing accurate and robust self-localization using a low-cost RFID system. The design of a likelihood model of RFID tag detection is also crucial for the accurate self-localization. This paper presents a novel design and arrangement of RFID readers and tags for indoor mobile robot self-localization. First, by considering small-sized and large-sized antennas of an RFID reader, we show how the design of the likelihood model affects the accuracy of self-localization. We also design a novel likelihood model by taking into consideration the characteristics of the communication range of an RFID system with a large antenna. Second, we propose a novel arrangement of RFID tags with eight RFID readers, which results in the RFID system configuration requiring much fewer readers and tags while retaining reasonable accuracy of self-localization. We verify the performances of MCL-based self-localization realized using the high-frequency (HF)-band RFID system with eight RFID readers and a lower density of RFID tags installed on the floor based on MCL in simulated and real environments. The results of simulations and real environment experiments demonstrate that our proposed low-cost HF-band RFID system realizes accurate and robust self-localization of an indoor mobile robot.

Developing a computational solution of mobile robot self-localisation problem

This paper presents a computational solution for the mobile robot self-localization problem using a new adaptive data fusion structure. Input sensors are employed for feeding the fusion structure, and consequently more precise perception from a highly noisy environment can be obtained. This adaptation process relies on two main steps; firstly, to choose the sensors and secondly, employing a model adaptation process. Using this process, the more incorrect data are eliminated, the more accurate localization results are obtained. Also, implementing more accurate sensor models, the values of error obtained in the final iterations will be less. By implanting the proposed process, simulation results indicate that the proposed structure is very useful and effective, especially when compared to the conventional techniques of localization problems solutions which rely mainly on the information theory.

2A1-U02 カメラ画像内の路面エッジ検出による屋外自律ロボットの自己位置・姿勢推定の検証

2A1-U02 カメラ画像内の路面エッジ検出による屋外自律ロボットの自己位置・姿勢推定の検証