Younggun Cho

Dynamic Ride Quality Investigation for Passenger Car

In this study the ride values of passenger cars are investigated for Korean subjects based on the vibration of the human bodies. When three subjects are excited by driving a vehicle on the road, their responses of acceleration are measured at 12 points on their bodies according to Griffins 12 axis system: 3 translational axes on a seat surface, 3 rotational axes on a seat surface, 3 translational axes at the seat back, and the 3 translational axes at the feet. Since one of the most important parameters for ride comfort is the level and duration of the root mean square acceleration experienced, the following ride values are evaluated for four different vehicles: the seat effective amplitude transmissibility (SEAT), the component ride value, and the overall ride value based on acceleration root mean square. For this purpose, frequency weighing functions and axis multiplying factors are used. The ride indices are also studied considering the seat dynamic characteristics with subjects. (A) For the covering abstract of the conference see IRRD 492369.

DETERMINATION OF SEAT SPONGE PROPERTIES WITH ESTIMATED BIODYNAMIC MODEL

This paper deals with the determination of the seat sponge parameters by using the estimated nine degree-of-freedom (DOF) biodynamic model. The suggested nine DOF model has multiple outputs that include the major axes for evaluating the ride quality in vehicles such as z-axis of the floor, hip, and x-axis of the back, in addition to the z-axis of the head for describing the whole-body vibration. It is intended to resemble the sitting posture with backrest support. Three experiments were executed to validate the proposed models. The first one was to measure the acceleration of the floor and hip in z-axis, the back in x-axis, and the head in z-axis under exciter. The second one was the measurement of joint positions, and contact points between the human body and the seat. The third one was the dropping test to measure the seat and back cushion. The biodynamic model parameters were obtained by matching the simulated to the experimental transmissibilities at the hip, back, and head. From this estimated model, the optimal seat parameters are determined to minimize the overall ride value at the floor, hip, and back on three kinds of roads (highway, Korean national road, and unpaved road), with the constraint that the stiffness of sponge has a roughly linear relation with the damping. Concerning the optimal seat sponge characteristics it is found that the lower stiffness and damping transmits the lower vibration to the human body. For the covering abstract of the conference see ITRD E206480.

Accurate Mobile Urban Mapping via Digital Map-Based SLAM

This paper presents accurate urban map generation using digital map-based Simultaneous Localization and Mapping (SLAM). Throughout this work, our main objective is generating a 3D and lane map aiming for sub-meter accuracy. In conventional mapping approaches, achieving extremely high accuracy was performed by either (i) exploiting costly airborne sensors or (ii) surveying with a static mapping system in a stationary platform. Mobile scanning systems recently have gathered popularity but are mostly limited by the availability of the Global Positioning System (GPS). We focus on the fact that the availability of GPS and urban structures are both sporadic but complementary. By modeling both GPS and digital map data as measurements and integrating them with other sensor measurements, we leverage SLAM for an accurate mobile mapping system. Our proposed algorithm generates an efficient graph SLAM and achieves a framework running in real-time and targeting sub-meter accuracy with a mobile platform. Integrated with the SLAM framework, we implement a motion-adaptive model for the Inverse Perspective Mapping (IPM). Using motion estimation derived from SLAM, the experimental results show that the proposed approaches provide stable bird’s-eye view images, even with significant motion during the drive. Our real-time map generation framework is validated via a long-distance urban test and evaluated at randomly sampled points using Real-Time Kinematic (RTK)-GPS.

Estimation of ambient light and transmission map with common convolutional architecture

This paper presents a method for effective ambient light and transmission estimation in underwater images using a common convolutional network architecture. The estimated ambient light and the transmission map are used to dehaze the underwater images. Dehazing underwater images is especially challenging due to the unknown and significantly varying ambient light in underwater environments. Unlike common dehazing methods, the proposed method is capable of estimating ambient light along with the transmission map thereby improving the reconstruction quality of the dehazed images. We evaluate the dehazing performance of the proposed method on real underwater images and also compare our method to current state-of-the-art techniques.

Road-SLAM : Road marking based SLAM with lane-level accuracy

In this paper, we propose the Road-SLAM algorithm, which robustly exploits road markings obtained from camera images. Road markings are well categorized and informative but susceptible to visual aliasing for global localization. To enable loop-closures using road marking matching, our method defines a feature consisting of road markings and surrounding lanes as a sub-map. The proposed method uses random forest method to improve the accuracy of matching using a sub-map containing road information. The random forest classifies road markings into six classes and only incorporates informative classes to avoid ambiguity. The proposed method is validated by comparing the SLAM result with RTK-Global Positioning System (GPS) data. Accurate loop detection improves global accuracy by compensating for cumulative errors in odometry sensors. This method achieved an average global accuracy of 1.098 m over 4.7 km of path length, while running at real-time performance.

Online depth estimation and application to underwater image dehazing

Underwater images captured in a turbid medium often suffer from significant degradation of visibility. Conventional dehazing approaches focus on dehazing a single image by using multiple channels for color restoration and rarely consider computational efficiency. This paper proposes an online dehazing method with sparse depth priors using an incremental Gaussian Process (iGP). The main contribution of this paper is developing a practically usable dehazing method for underwater robots using incoming sparse depth priors (range measurements) from any calibrated depth sensors. To deal with incoming depth priors efficiently, we adopt iGP for incremental depth map estimation and dehazing. Because the input vector of the iGP model is easily reconfigured, we can use the same update method for both color and gray images. Our method also estimates color-balanced veiling light to compensate for the color attenuation problem. For the evaluation, we first verify the proposed method on a open RGBD dataset and test it on real underwater color and gray images, comparing the results with those of previous methods.

Visibility enhancement for underwater visual SLAM based on underwater light scattering model

This paper presents a real-time visibility enhancement algorithm for effective underwater visual simultaneous localization and mapping (SLAM). Unlike an aerial environment, an underwater environment contains larger particles and is dominated by a different image degradation model. Our method starts with a thorough understanding of underwater particle physics (e.g., forward, back, multiple scattering, blur and noise). Targeting underwater image enhancement in a real-world application, we include an artificial light model in the derivation. The proposed method is effective for both color and gray images with substantial improvement in the process time compared to conventional methods. The proposed method is validated by using simulated synthetic images (color) and real-world underwater images (color and grayscale). Using two underwater image sets acquired from the same area but with different water turbidity, we evaluate the proposed visibility enhancement and camera registration improvement in SLAM.

Position estimation of landmark using 3D point cloud and trilateration method

We demonstrate the appropriacy of using laser distance meter as the accurate, cost-effective, simple solution for position estimation within precise 3D point cloud map in urban environment scenario. Our approach treats trilateration method with minimum error selection algorithm for better position accuracy of landmark. We validate the performance of our approach through 22 landmarks measured using RTK GPS for error analysis of proposed position estimation method based on trilateration.

Channel invariant online visibility enhancement for visual SLAM in a turbid environment

This paper presents a real-time and channel-invariant visibility enhancement algorithm using a hybrid image enhancement approach. The proposed method is initially motivated by an underwater visual simultaneous localization and mapping (SLAM) failure in a turbid medium. The environments studied contain various particles and are dominated by a different image degradation model. Targeting image enhancement for degraded images but not being limited to it, the proposed method provides a highly effective solution for both color and gray images with substantial improvement in the process time compared to conventional methods. The proposed method introduces a hybrid scheme of two image enhancement modules: a model-based (extensive) enhancement and a model-free (immediate) enhancement. The proposed method is validated by using simulated synthetic color images and real-world color and grayscale underwater images. Real world validation is performed in various environments such as hazy indoor, smoky indoor, and underwater. Using the ground truth trajectory or clear images acquired from the same area but without turbidity, we evaluate the proposed visibility enhancement and camera registration improvement for a feature based (ORB-SLAM2), a direct (LSD-SLAM) and a visual underwater SLAM application.

LiDAR configuration comparison for urban mapping system

The Mobile Mapping System (MMS) is widely used when mapping urban environment. The critical challenge for mapping accuracy is at localization accuracy under highly sporadic global positioning system (GPS) signal. To tackle this issue, approaches often rely on cameras and Light Detection and Ranging (LiDAR)s to exploit visual and spatial features in the environment. Among many sensors, this paper focuses on the use of LiDAR, especially evaluating the LiDAR types and mechanical configurations. In this paper, we compare two typical LiDAR configurations, push-broom (2D) and 360 scanning (3D) style, in terms of the resulting mapping performance. Resulting maps from two configurations over the same place are directly compared to evaluate characteristic of each LiDAR configuration.