Hyun Chul Roh

Video stabilization for robot eye using IMU-aided feature tracker

In this paper, new video stabilization system is presented for robot eye. This system is biologically inspired by the human vestibulo-ocular reflex. Feature tracker with inertial sensor is proposed to estimate the motion more accurately and fast. The rotational motion measured by the inertial sensor is incorporated into the KLT tracker in order to predict a position of feature in current frame. This IMU-aided tracker improves a success rate and reduces an iteration number in tracking feature. Also, a Kalman filter is applied to remove unwanted camera motion. The experimental results show that the proposed video stabilization system has the characteristics of the high speed and accuracy in various conditions.

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.

Long-time video stabilization using point-feature trajectory smoothing

This paper presents a new video stabilization algorithm for digital video cameras. The proposed technique directly estimates correction motion through point-feature trajectories, so that our method can be free from the error accumulation as time goes on. Furthermore, an adaptive RANSAC is proposed to estimate a robust correction motion. Experimental results show that the proposed algorithm can robustly stabilize very long videos in the end.

Digital Image Stabilization for humanoid eyes inspired by human VOR system

In this paper, a new Digital Image Stabilization (DIS) system is for humanoid eyes presented. This DIS system was biologically inspired by the human Vestibulo-Ocular Reflex (VOR) system, and is based on a KLT tracker and an Inertial Measurement Unit (IMU). In order to more accurately estimate the motion between two consecutive image frames, corresponding points that were discovered by KLT tracker and IMU were used. The initial motion that was estimated with the IMU was incorporated into the KLT tracker in order to improve the speed and accuracy of the tracking process. Also, a Kalman filter was applied to remove unwanted camera motion. The experimental results showed that the proposed DIS system has the characteristics of the high speed and accuracy in various conditions.

Fast SLAM using polar scan matching and particle weight based occupancy grid map for mobile robot

Simultaneous Localization and Mapping is the one of essential techniques for mobile robot navigation. In this paper, we propose a fast SLAM method that uses particle weight based occupancy grid map, and also works well with in indoor environment. In addition, in the prediction step, our method that uses the polar scan matching and wheel odometry information improves the accuracy of prediction pose. In order to evaluate our proposed method, we use a simulation and the 2-wheeled mobile robot that is able to move in large-scale indoor environment. The experimental results show that our method is suitable for fast and accurate mapping and localization algorithm for mobile robot.

3D video stabilization for humanoid eyes using vision and inertial sensors inspired by human VOR

This paper proposes a new 3D video stabilization for humanoid eyes using vision and inertial sensors. Most existing video stabilization methods based on 2D motion model work well where scenes are far from the camera or can be modeled as a plane. But these 2D video stabilizations may fail to stabilize unstable videos because a humanoid robot operates in the real 3D world. And so far 3D video stabilization approaches cannot be directly applied to humanoid robot eyes because they use an off-line optimization method. In order to solve above problems, we propose 3D video stabilization based on a spatially-varying warping technique that uses sparse reconstructed 3D data. And we integrate the proposed 3D video stabilization into a hardware stabilization device for humanoid eyes inspired by human VOR in order to compensate large and rapid unwanted motions. The experimental results showed that the proposed 3D video stabilization system can be a potential candidate for humanoid eyes.

3D video stabilization for a humanoid robot using point feature trajectory smoothing

This paper proposes a new 3D video stabilization method for a humanoid robot without explicit global camera motion estimation with respect to a reference frame. 2D video stabilization that uses a 2D camera motion model works well if scenes are far from the camera of the robot or can be modeled as a plane. However, because a humanoid robot operates in a real 3D space, these 2D video stabilization approaches may fail to stabilize unstable videos. Furthermore, straightforward 3D video stabilization methods that depend on structure-from-motion to estimate global camera motion might fail in the long run because error accumulation in the global motion estimation step is inevitable with the elapse of time. Instead, our method uses only the relative 3D camera motion between the real unstable camera and the virtual stabilized camera through point feature trajectory smoothing. In order to evaluate our method, we use a real humanoid robot that is able to walk and run. The experimental results show that the proposed 3D video stabilization system is a potential candidate for application in humanoid robots.

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.

Rapid SLAM using simple map representation in indoor environment

Simultaneous Localization and Mapping is the one of essential techniques for mobile robot navigation. In this paper, we propose rapid SLAM using simple map representation in indoor environment for mobile robot. This approach offers a new way to look at the problem focusing on the issues that have caused the use of 3D laser scanner which provide lot of 3D point data. We have tried to create the simple segments of line and range table for scan-matching in a way that allows a robust solution to the problem. In this article, two important issues of this work on 2D registrations are made. First, it is shown that the algorithm performs very well on the transformations of segmented line map from lot of 3D point cloud data. Second the mapping logic and sequence are explained in this paper to a line component accumulation for a map building. Experimental results from 3-D sensor Velodyne and wheel-odometry data logger Racelogic Vbox are given. Experimental results show that this approach is not only robust for line mapping but it is also fast, requires significantly less memory.

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.