Yu-Cheol Lee

Evaluation of features through grid association for building a sonar map

This paper addresses a new feature map building method that can minimizes the appearance of phantom features by using only sparse sonar data. The approach is composed of extraction of features and building a probability grid map using only the footprint of sparse sonar data, estimation of position uncertainty of the feature, and evaluation of the reliable features. A virtual circle association frame model has been developed, which associates two sonar footprints into a virtual circle frame. Using this model, the geometric primitives such as lines, points, and arc features are separately extracted. While extracting the features, a grid map is also built using the orientation probability approach. The position uncertainty of each extracted feature is, then, estimated by considering both the position uncertainty of the robot and the measurement uncertainty of the sonar sensor. Finally, the reliable features among all extracted ones are evaluated from grid association method. The proposed methods have been tested in a real home environment with a mobile robot

Sonar Map Construction for Autonomous Mobile Robots Using a Data Association Filter

This paper presents a method for building a probability grid map for autonomous mobile robots with ultrasonic sensors using a data association filter (DAF). The method is based on evaluating the possibility that the acquired sonar data are all reflected by the same object. The DAF is able to associate data points with each other. Data affected by specular reflection are not likely to be associated with the same object, so they are excluded from the data cluster by the DAF, thereby improving the reliability of the data used for the probability grid map. Since the corrupted data are not used to update the probability map, it is possible to build a good quality grid map even in a specular environment. The DAF was applied to the Bayesian and the Orientation probability models, which are typical models used to build grid maps, to verify its effectiveness. Experimental results were also obtained using a mobile robot in a real-world environment.

Sonar Grid Map Based Localization for Autonomous Mobile Robots

A mobile robot must be able to build a reliable map of surroundings and estimate its position. We have developed a technique for a grid-based localization of a mobile robot with ultrasonic sensors using extended Kalman filter (EKF). For this, we used grids themselves as landmarks of the environment. The grid-based localization can minimize the use of computer resources for localization because this approach does not rely on exact geometric representation of a landmark. Experiments were performed in a real environment to verify the methodology developed in this study, and the results indicate that the grid-based localization can be useful for a practical application.

Adaptive localization for mobile robots in urban environments using low-cost sensors and enhanced topological map

This paper presents a technique for accurate localization of mobile robots using an enhanced topological map and using the low-cost sensors such as wheel odometer, global positioning system (GPS), and mono-camera. The localization framework is based on EKF to fuse the sensor data and the topological map. The sensor data include the positions of traffic marks measured by camera and topological map having the actual positions of traffic marks extracted from aerial or satellite images in advance. Our approach obtains the adaptive parameter for EKF localization by matching two positions, measured by camera and extracted from topological map, on each traffic mark. The adaptive parameter reflects the geographical characteristics, e.g. hill, corner, and road surfaces. The proposed method has shown high accuracy result and apparently better performance of the EKF localization with adaptive parameter. The proposed method is economically feasible and practically applicable to commercial robots using the low-cost sensors and providing the reliable localization services.

Artificial landmark map building method based on grid SLAM in large scale indoor environment

This paper proposes the artificial landmark map building method using a Grid-based Simultaneous Localization And Mapping (Grid SLAM). The Grid SLAM method is employed to simultaneously localize the position of mapping cart and construct the map of working area. Based on the estimated position of the mapping cart and the grid-based map, the artificial landmarks are localized and their positions are saved to the artificial landmark map. The proposed method reduces the complexity and the cost of mapping process which is usually done by hand. The real implementation has been carried out to build the artificial landmark map of large scale indoor environment named T-City. The implementation results show that the proposed method gives a convenient way to construct the artificial landmark map while still maintaining the accuracy of map. The correctness of the artificial landmark map has been confirmed through the real operation of the mobile robots which rely on the artificial landmark map for their navigation at T-City.

Trajectory generation method using Bézier spiral curves for high-speed on-road autonomous vehicles

This paper proposes a trajectory generation method for high-speed autonomous vehicles in a structured on-road environment, such as a highway. The proposed method generates a smooth trajectory using G2 cubic Bézier spiral smoothing, based on the current trajectory and the centerline model of a desired lane, and the velocity profile of the trajectory. Using the proposed method, an on-road vehicle can follow or change lanes safely. The performance of the proposed method was confirmed by offline experiments using a real data set and online experiments in a real environment.

3D map building method with mobile mapping system in indoor environments

This paper presents three dimensional (3D) map building method for the intelligent vehicles based on accurate indoor localization using a mobile mapping system (MMS) that is equipped with perception sensors consist of a wheel odometer, a laser range finder (LRF), and two projected texture stereo (PTS) cameras. The environmental data measured by perception sensors are stored in the node units according to a certain distance interval. In order to estimate the positions of the MMS using the relationship among nodes, the localization method is divided into two parts, front-end (map-based scan matching) and back-end (graph-based optimization). The estimated positions are used to build the grid-based map and the point cloud dataset, respectively as the 2D and the 3D maps through the mapping process (Bayesian model). An experiment has been performed in office environment (indoor) to verify the effectiveness of the proposed method. Experimental results show the high precision of 3D point cloud dataset that can be used for various applications including navigation of intelligent vehicles and pedestrians in indoor evironments.

Urban localization method for mobile robots based on dead reckoning sensors, GPS, and map matching

This paper presents a localization method in urban environments by using dead reckoning sensors, Global Positioning System (GPS), and taking into account the benefits of map matching. Extended Kalman Filter (EKF) is used as the main framework to fuse the information from sensors. However, the result of the EKF greatly depends on how the robot utilizes and judges the position measurement which comes from GPS since the GPS easily gives wrong position measurement due to the phenomenon called multipath effect. Under the assumption that the robot must operate only on the main road, a map matching is used to filter out the wrong GPS measurements which fall outside the main road. An experiment has been conducted in urban environment to validate the proposed method. Experimental results show that our proposed method has superior performance compared to the EKF without map matching

Applications of robot navigation based on artificial landmark in large scale public space

This paper presents the navigation technique for service mobile robots in large scale environment using artificial landmark. The robot navigation is comprised of three major components; localization, mapping, and path planning. To implement the stable navigation system of mobile robots, many developers could use the artificial landmark to measure the accurate position of robot in large scale environment. When the mobile robots obtains the position based on the artificial landmark, they need to have the map which contains information about the positions of artificial landmarks. In this paper, Grid-based Simultaneous Localization And Mapping (Grid SLAM) method is employed to construct the map of artificial landmark using a specially made mapping cart. The artificial landmark map has been used for the real operation of four kinds of robots; patrol, guidance, delivery, cafeteria serving, relying on the artificial landmark map for their navigation at the large scale indoor environment named Tomorrow City (T-City) where the artificial landmark system is installed for the robotic technologies. The implementation results show that it is possible for mobile robots to provide various services based on the artificial landmark in large indoor environment.

Localization method for mobile robots moving on stairs in multi-floor environments

This paper presents about the localization technology to estimate the positions of the mobile robot that is able to navigate through the multi-floor spaces, for expanding the application ranges of the robots. For this purpose, the mobile robots can use some maps having some information about the spaces in advance. In this paper, the maps are divided into two types according to the application; as to one, it is the grid-based map that represents the locations and the structures of objects in the robot surroundings with each floor. The other is the inter-floor map containing the stairs data to help the robot move into other floors. And the localization method is consisted of the floor and the inter-floor localizations. The floor localization uses the particle filter method to estimate the robot position in each floor. And the inter-floor localization can help the robot safely move to other floors by recognizing the stairs and change the coordinate system. Finally we performed the experiments in the multi-floor office spaces to verify the effectiveness of the proposed localization method by using the robot with the caterpillar wheels and the maps.