• 제어로봇시스템학회논문지
• /
• 제19권8호
• /
• pp.745-754
• /
• 2013

An efficient urban outdoor localization and navigation system is proposed for car-like robots. First an accurate outdoor localization method is suggested using line/arc features and 2.5D map matching with LRFs (Laser Range Finders), which can reduce the number of singular cases and increase accuracy. Also, path generation, path tracking, and path modification algorithms are proposed for navigation. All these algorithms are implemented on an electric scooter to construct an autonomous urban outdoor localization and navigation system. Experiments reveal the practicality of the proposed system.

• 제어로봇시스템학회논문지
• /
• 제15권2호
• /
• pp.203-210
• /
• 2009

The problem of outdoor localization can be practically solved by GPS. However, GPS is not perfect and some areas of outdoor navigation should consider other solutions. This research deals with outdoor localization using an elevation map without GPS. This paper proposes a novel feature, elevation moment of inertia (EMOI), which represents the distribution of elevation as a function of distance from a robot in the elevation map. Each cell of an elevation map has its own EMOI, and outdoor localization can be performed by matching EMOIs obtained from the robot and the pre-given elevation map. The experiments and simulations show that the proposed EMOI can be usefully exploited for outdoor localization with an elevation map and this feature can be easily applied to other probabilistic approaches such as Markov localization method.

• 센서학회지
• /
• 제19권3호
• /
• pp.246-257
• /
• 2010

Autonomous guided robot navigation which consists of following unknown paths and avoiding unknown obstacles has been a fundamental technique for unmanned robots in outdoor environments. The unknown path following requires techniques such as path recognition, path planning, and robot pose estimation. In this paper, we propose a novel sensor fusion system for autonomous guided robot navigation in outdoor environments. The proposed system consists of three monocular cameras and an array of nine infrared range sensors. The two cameras equipped on the robot's right and left sides are used to recognize unknown paths and estimate relative robot pose on these paths through bayesian sensor fusion method, and the other camera equipped at the front of the robot is used to recognize abrupt curves and unknown obstacles. The infrared range sensor array is used to improve the robustness of obstacle avoidance. The forward camera and the infrared range sensor array are fused through rule-based method for obstacle avoidance. Experiments in outdoor environments show the mobile robot with the proposed sensor fusion system performed successfully real-time autonomous guided navigation.

• Journal of Positioning, Navigation, and Timing
• /
• 제12권4호
• /
• pp.399-408
• /
• 2023

In this paper, we propose a solution that enables continuous indoor/outdoor positioning of smartphone users through the integration of Pedestrian Dead Reckoning (PDR) and GPS/WiFi signals. Considering that accurate step detection affects the accuracy of PDR, we propose a Deep Neural Network (DNN)-based technology to distinguish between walking and non-walking signals such as walking in place. Furthermore, in order to integrate PDR with GPS and WiFi signals, a technique is used to select a proper measurement by distinguishing between indoor/outdoor environments based on GPS Dilution of Precision (DOP) information. In addition, we propose a technology to adaptively change the measurement error covariance matrix by detecting measurement outliers that mainly occur in the indoor/outdoor transition section through a residual-based χ² test. It is verified through experiments on a testbed that these technologies significantly improve the performance of PDR and PDR/GPS/WiFi fingerprinting-based integrated pedestrian navigation.

• 제어로봇시스템학회논문지
• /
• 제18권6호
• /
• pp.594-600
• /
• 2012

Accurate localization is very important to stable navigation of a mobile robot. This paper deals with local localization of a mobile robot especially for outdoor environments. The GPS information is the easiest way to obtain the outdoor position information. However, the GPS accuracy can be severely affected by environmental conditions. To deal with this problem, the GPS and wheel odometry can be combined using an EKF (Extended Kalman Filter). However, this is not enough for safe navigation of a mobile robot in outdoor environments. This paper proposes a novel method using lane features from the road image. The pose data of a mobile robot can be corrected by analyzing the detected lane features. This can improve the accuracy of the localization process substantially.

• 제어로봇시스템학회논문지
• /
• 제15권3호
• /
• pp.293-299
• /
• 2009

This paper discusses an autonomous navigation system for urban environments. For the localization of the robot, EKF (Extended Kalman Filter) algorithm is used with odometry, angle sensor, and DGPS (Differential Global Positioning System) measurement. Especially in an urban environment, DGPS is often blocked by buildings and trees and the resulting inaccurate positioning prevents the robot from safe and reliable navigation. In addition to the global information from DGPS, the local information of the curb on the roadway is used to track a route when the global DGPS information is inaccurate. For this purpose, curb detection algorithm is developed and implemented in the developed navigation algorithm. Four different types of navigation strategies are developed and they are switched to adapt to different localization conditions according to the availability of DGPS and the existence of the curbs on the roadway. The experimental results show that the designed switching strategy improves the navigation performance adapting to the environment conditions.

• 제어로봇시스템학회논문지
• /
• 제21권8호
• /
• pp.773-780
• /
• 2015

Navigation in outdoor environments is a fundamental and challenging problem for unmanned ground vehicles. Detecting lane markings or boundaries on the road may be one of the solutions to make navigation easy. However, because of various environments and road conditions, a robust lane detection is difficult. In this paper, we propose a new approach for estimating virtual lanes on a traversable region. Estimating the virtual lanes consist of two steps: (i) we detect virtual road region through road model selection based on traversability at current frame and similarity between the interframe and (ii) we estimate virtual lane using the number of lane on the road and results of previous frame. To improve the detection performance and reduce the searching region of interests, we use a probability map representing the traversability of the outdoor terrain. In addition, by considering both current and previous frame simultaneously, the proposed method estimate more stable virtual lanes. We evaluate the performance of the proposed approach using real data in outdoor environments.

• 한국군사과학기술학회지
• /
• 제14권2호
• /
• pp.271-280
• /
• 2011

This paper presents a navigation system based on the tactical communication and vision system in outdoor environments which is applied to unmanned robot for perimeter surveillance operations. GPS errors of robot are compensated by the reference station of C2(command and control) vehicle and WiBro(Wireless Broadband) is used for the communication between two systems. In the outdoor environments, GPS signals can be easily blocked due to trees and buildings. In this environments, however, vision system is very efficient because there are many features. With the feature MAP around the operation environments, the robot can estimate the position by the image matching and pose estimation. In the navigation system, thus, operation modes is switched by navigation manager according to some environment conditions. The experimental results show that the unmanned robot can estimate the position very accurately in outdoor environment.

• Journal of Positioning, Navigation, and Timing
• /
• 제1권1호
• /
• pp.35-43
• /
• 2012

The UGV is one of the special field robot developed for mine detection, surveillance and transportation. To achieve successfully the missions of the UGV, the accurate and reliable navigation data should be provided. This paper presents design and implementation of multi-sensor-based open architecture integrated navigation for localization of UGV. The presented architecture hierarchically classifies the integrated system into four layers and data communications between layers are based on the distributed object oriented middleware. The navigation manager determines the navigation mode with the QoS information of each navigation sensor and the integrated filter performs the navigation mode-based data fusion in the filtering process. Also, all navigation variables including the filter parameters and QoS of navigation data can be modified in GUI and consequently, the user can operate the integrated navigation system more usefully. The conventional GPS/INS integrated system does not guarantee the long-term reliability of localization when GPS solution is not available by signal blockage and intentional jamming in outdoor environment. The presented integration algorithm, however, based on the adaptive federated filter structure with FDI algorithm can integrate effectively the output of multi-sensor such as 3D LADAR, vision, odometer, magnetic compass and zero velocity to enhance the accuracy of localization result in the case that GPS is unavailable. The field test was carried out with the UGV and the test results show that the presented integrated navigation system can provide more robust and accurate localization performance than the conventional GPS/INS integrated system in outdoor environments.

• 한국항해항만학회:학술대회논문집
• /
• 한국항해항만학회 2006년도 International Symposium on GPS/GNSS Vol.1
• /
• pp.251-256
• /
• 2006

In the work package 'Integrated Positioning' of the research project NAVIO (Pedestrian Navigation Systems in Combined Indoor/Outdoor Environements) we are dealing with the navigation and guidance of visitors of our University. Thereby start points are public transport stops in the surroundings of the Vienna University of Technology and the user of the system should be guided to certain office rooms or persons. For the position determination of the user different location sensors are employed, i.e., for outdoor positioning GPS and dead reckoning sensors such as a digital compass and gyro for heading determination and accelerometers for the determination of the travelled distance as well as a barometric pressure sensor for altitude determination and for indoor areas location determination using WiFi fingerprinting. All sensors and positioning methods are combined and integrated using a Kalman filter approach. Then an optimal estimate of the current location of the user is obtained using the filter. To perform an adequate weighting of the sensors in the stochastic filter model, the sensor characteristics and their performance was investigated in several tests. The tests were performed in different environments either with free satellite visibility or in urban canyons as well as inside of buildings. The tests have shown that it is possible to determine the user's location continuously with the required precision and that the selected sensors provide a good performance and high reliability. Selected tests results and our approach will be presented in the paper.