nan Christiand

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.

Autonomous navigation of mobile robot based on DGPS/INS sensor fusion by EKF in semi-outdoor structured environment

Although GPS/DGPS become the dominant localization solution in the outdoor environment, it needs assistant sensors or algorithms for the covering the area not to get the position information from GPS. Especially, in the robot navigation, the sensor fusion algorithm is needed. In addition, it is hard to get the position information at the area surrounded the high buildings such as the downtown because GPS signals is so feeble. Therefore, this paper illustrates an efficient method for the outdoor localization incorporating DGPS, Encoder, and IMU sensor based on EKF. To show the localization performances of the proposed fusion algorithm, we have implemented the proposed algorithm and applied the advertising robot platform which is operating well during 80 days in the real semi-outdoor structured environment. The proposed sensor fusion algorithm and the experimental results showed the feasibility of our novel sensor fusion algorithm.

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.

An enhanced haptic assembly simulation system for the efficiency of assembly tasks

This paper describes an enhanced haptic assembly simulation system, in which an optimal assembly algorithm is used to allow haptic interactions and traditional assembly sequence problems. The optimal assembly algorithm provides optimal paths for haptic guidance as well as an assembly sequence of the parts to be assembled. The performance of the given assembly schemes were simulated and analyzed using a haptic assembly system. Experimental results showed that the haptic-path sequence-guidance (HSG) mode gave the best performance improvement in terms of accumulated assembly time (28.56%) and travel distance (15.64%) compared to the unguided mode, while the sequence-guidance (SG) mode alone increased performance by 16.91% for assembly time and 11.66% for travel distance. The experimental results were analyzed by the sub-tasks of gripper selection, inter-part movement, and part assembly which showed the effectiveness of the optimal assembly algorithm.

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.

Satellite image based topological map building method for intelligent mobile robots

This paper presents an efficient method for building a topological map for robots in urban environments based on satellite image maps acquired from a geographic information system (GIS). In urban space, mobile robots need a special map, such as a topological map, to generate a path toward their goal. A topological map for mobile robot navigation should include semantic data, e.g., the width and type of road. We divide the satellite image based topological map building process into two steps. The first step is defining the topological map model to fit the mobile robot navigation in an urban environment. The second step is generating a topological map from existing satellite image maps downloaded from the GIS to reduce the cost and improve the accuracy in building the map. If a GIS-based topological map is constructed once, it is possible to use the map for the navigation of various robots as common spatial data. To validate the proposed mapping method, we show details of its implementation in map building as well as in a navigation experiment in real outdoor environments, such as a campus or city district.

EKF localization with lateral distance information for mobile robots in urban environments

This paper proposes the EKF localization with lateral distance information for the mobile robots in urban environments. The mobile robot is assumed to be operated at common roads where the lane marker exists. The lateral distance information given by the lane marker detector is used to compensate the measurement of global positioning system (GPS) before the EKF update step. In addition, the proposed method also uses Mahalanobis distance approach to validate the sensors measurements. The GPS compensation and Mahalanobis distance validation effectively reduce the sensors error, in particular drift error and jumping position of GPS. As a result, the accuracy of the estimated position is better than normal EKF localization.

Topological map building for mobile robots based on GIS in urban environments

This paper presents an efficient method for building a topological map for robots in urban environments based on a geographic information system (GIS) such as satellite maps. In urban space, mobile robots need a special map, such as a topological map, to generate a path toward their goal. Unlike a car map, a topological map for mobile robot navigation should include semantic data, e.g., the width and type of road. This paper divides the GIS-based topological map building process into two steps. The first step is defining the topological map model to fit the mobile robot navigation in an urban environment. The second step is generating a topological map from existing GIS data to reduce the cost and improve the accuracy in building the map.

2D RGB depth map building for outdoor mobile robots using Particle Filter

This paper describes the RGB depth (RGB-D) map building for mobile robots based on accurate outdoor localization and perception sensors consisting of wheel odometer, global positioning system (GPS), and camera and laser range finder (LRF). A localization method based on Particle Filter (PF) is used to integrate the sensor data and the topological map. The sensors data include geo-locations, the relative moving positions and the traffic mark positions measured by GPS, odometer and camera. The topological map has information for converting domains between geo- and metric- locations of GPS and odometer. And it also gives the actual positions of traffic marks extracted from aerial or satellite images. In addition, we used also a 2D RGB-D map building method by matching information between RGB and depth by camera and LRF at estimated position from PF. An experiment has been performed in outdoor environment to validate the proposed method. Experimental results show the high accuracy RGB-D map that is able to use for navigation of mobile robot in outdoor environments.