• Journal of Positioning, Navigation, and Timing
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• v.2 no.1
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• pp.81-89
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• 2013

Many of the current visual odometry algorithms suffer from some extreme limitations such as requiring a high amount of computation time, complex algorithms, and not working in urban environments. In this paper, we present an approach that can solve all the above problems using a single camera. Using a planar motion assumption and Ackermann's principle of motion, we construct the vehicle's motion model as a circular planar motion (2DOF). Then, we adopt a 1-point method to improve the Ransac algorithm and the relative motion estimation. In the Ransac algorithm, we use a 1-point method to generate the hypothesis and then adopt the Levenberg-Marquardt method to minimize the geometric error function and verify inliers. In motion estimation, we combine the 1-point method with a simple least-square minimization solution to handle cases in which only a few feature points are present. The 1-point method is the key to speed up our visual odometry application to real-time systems. Finally, a Bundle Adjustment algorithm is adopted to refine the pose estimation. The results on real datasets in urban dynamic environments demonstrate the effectiveness of our proposed algorithm.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.194-202
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• 2008

This paper describes a new method for indoor environment mapping and localization with stereo camera. For environmental modeling, we directly use the depth and color information in image pixels as visual features. Furthermore, only the depth and color information at horizontal centerline in image is used, where optical axis passes through. The usefulness of this method is that we can easily build a measure between modeling and sensing data only on the horizontal centerline. That is because vertical working volume between model and sensing data can be changed according to robot motion. Therefore, we can build a map about indoor environment as compact and efficient representation. Also, based on such nodes and sensing data, we suggest a method for estimating mobile robot positioning with random sampling stochastic algorithm. With basic real experiments, we show that the proposed method can be an effective visual navigation algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.36.4-36
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• 2001

A robust and reliable path recognition system is one necessary component for the autonomous navigation of a mobile robot to help determining its current position in its navigation map. This paper describes a computer visual path-recognition system using on-board video camera as vision-based driving assistance for an autonomous navigation mobile robot. The common problem for a visual system is that its reliability was often influenced by different lighting conditions. Here, two different image processing methods for the path detection were developed to reduce the effect of the luminance: one is based on the RGB color model and features of the path, another is based on the HSV color model in the absence of luminance.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.3
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• pp.242-250
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• 2003

We propose the monitoring system scheme for the CPS/INS integrated navigation system. The design requirements of the monitoring system are suggested and the software scheme based on GUI is proposed. The proposed monitoring system consists of an I/O interface part, a navigation data display part, and a post-processing part. The I/O interface part is responsible for data communication between the monitoring system and a navigation computer unit. The navigation data display part provides various display methods to show the navigation data to user in real-time. The post-processing part collects the navigation data to analyze the performance of navigation system. The proposed monitoring system software was developed using the Visual C++ programming language and a van test was carried out to demonstrate the real-time operation of the monitoring system. The test result shows that the proposed monitoring system can be effectively applied to the CPS/INS integrated navigation system.

• International Journal of Control, Automation, and Systems
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• v.5 no.1
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• pp.51-60
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• 2007

This paper describes a visual tracking control law of an Unmanned Aerial Vehicle(UAV) for monitoring of structures and maintenance of bridges. It presents a control law based on computer vision for quasi-stationary flights above a planar target. The first part of the UAV's mission is the navigation from an initial position to a final position to define a desired trajectory in an unknown 3D environment. The proposed method uses the homography matrix computed from the visual information and derives, using backstepping techniques, an adaptive nonlinear tracking control law allowing the effective tracking and depth estimation. The depth represents the desired distance separating the camera from the target.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2008.10a
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• pp.5-8
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• 2008

This study proposed the Knowledge Base Building method reflecting the user's preferences based on the fuzzy set theory to develop information contents which support pedestrian's navigation. This research evaluated subject's preferences on the commercial spaces set to the hypothetical destination. Also it surveyed the causal relationship between the visual characteristics and the emotional characteristics to propose the methods of Navigation Knowledge Base (NKB). The NKB was composed by three elements; 1.the correlation model between emotional characteristics, 2.the causal relationship between visual characteristics and emotional characteristics, 3.the transformation model between visual characteristics and the physical characteristics.

• IEIE Transactions on Smart Processing and Computing
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• v.4 no.4
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• pp.216-223
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• 2015

The autonomous underwater vehicle (AUV) is being widely researched in order to achieve superior performance when working in hazardous environments. This research focuses on using image processing techniques to estimate the AUV's egomotion and the changes in orientation, based on image frames from different time frames captured from a single high-definition web camera attached to the bottom of the AUV. A visual odometry application is integrated with other sensors. An internal measurement unit (IMU) sensor is used to determine a correct set of answers corresponding to a homography motion equation. A pressure sensor is used to resolve image scale ambiguity. Uncertainty estimation is computed to correct drift that occurs in the system by using a Jacobian method, singular value decomposition, and backward and forward error propagation.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2017.11a
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• pp.248-248
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• 2017

• Journal of Mechanical Science and Technology
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• v.17 no.10
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• pp.1411-1422
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• 2003

The detection of free spaces between obstacles in a scene is a prerequisite for navigation of a mobile robot. Especially for stereo vision-based navigation, the problem of correspondence between two images is well known to be of crucial importance. This paper describes multi-range approach of area-based stereo matching for grid mapping and visual navigation in uncertain environment. Camera calibration parameters are optimized by evolutionary algorithm for successful stereo matching. To obtain reliable disparity information from both images, stereo images are to be decomposed into three pairs of images with different resolution based on measurement of disparities. The advantage of multi-range approach is that we can get more reliable disparity in each defined range because disparities from high resolution image are used for farther object a while disparities from low resolution images are used for close objects. The reliable disparity map is combined through post-processing for rejecting incorrect disparity information from each disparity map. The real distance from a disparity image is converted into an occupancy grid representation of a mobile robot. We have investigated the possibility of multi-range approach for the detection of obstacles and visual mapping through various experiments.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.47-49
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• 2022

Robot navigation has seen a major improvement since the the rediscovery of the potential of Artificial Intelligence (AI) and the attention it has garnered in research circles. A notable achievement in the area was Deep Learning (DL) application in computer vision with outstanding daily life applications such as face-recognition, object detection, and more. However, robotics in general still depend on human inputs in certain areas such as localization, navigation, etc. In this paper, we propose a study case of robot navigation based on deep reinforcement technology. We look into the benefits of switching from traditional ROS-based navigation algorithms towards machine learning approaches and methods. We describe the state-of-the-art technology by introducing the concepts of Reinforcement Learning (RL), Deep Learning (DL) and DRL before before focusing on visual navigation based on DRL. The case study preludes further real life deployment in which mobile navigational agent learns to navigate unbeknownst areas.