Jae-Hean Kim

Integration of a Rehabilitation Robotic System (KARES II) with Human-Friendly Man-Machine Interaction Units

In this paper, we report some important results of design and evaluation of a wheelchair-based robotic arm system, named as KARES II (KAIST Rehabilitation Engineering Service System II), which is newly developed for the disabled. KARES II is designed in consideration of surveyed necessary tasks for the target users (that is, people with spinal cord injury). At first, we predefined twelve important tasks according to extensive interviews and questionnaires. Next, based on these tasks, all subsystems are designed, simulated and developed. A robotic arm with active compliance and intelligent visual servoing capability is developed by using cable-driven mechanism. Various kinds of human-robot interfaces are developed to provide broad range of services according to the levels of disability. Eye-mouse, shoulder/head interface, EMG signal-based control subsystems are used for this purpose. Besides, we describe the process of integration of our rehabilitation robotic system KARES II, and discuss about user trials. A mobile platform and a wheelchair platform are two main platforms in which various subsystems are installed. For a real-world application of KARES II system, we have performed user trials with six selected potential end-users (with spinal cord injury).

SLAM with omni-directional stereo vision sensor

Vision sensors are attractive equipments for an autonomous mobile robot because they are information-rich and rarely have restrictions in range and various applications. However, there exists a difficult problem in using vision sensors to reconstruct geometrical information, which is a correspondence or matching problem. We present a simultaneous localization and map building (SLAM) algorithm for an autonomous mobile robot based on a vision sensor robust to the correspondence or matching problem with cooperation of structure from motion (SFM) and stereo. We use an omni-directional stereo vision sensor, which is suitable for SLAM. Due to its large field of view, we can acquire robust estimates and reduce the effect of motion drift from the fiducial coordinate frame. Results on real images illustrate the performance of the proposed method.

Absolute motion and structure from stereo image sequences without stereo correspondence and analysis of degenerate cases

This paper deals with the estimation of motion and structure with an absolute scale factor from stereo image sequences without stereo correspondence. We show that the absolute motion and structure can be determined using only motion correspondences. This property is very useful in two aspects: first, motion correspondence is easier to solve than stereo correspondence because sequences of images can be taken at short time intervals; second, it is not necessary that the rigid scene be included in the intersection of the field of view of the two cameras. It is also shown that the degenerate cases reported in this paper constitute all of the degenerate cases for the scheme and can be easily avoided.

Absolute Stereo SFM without Stereo Correspondence for Vision Based SLAM

This paper presents a vision based SLAM method by using stereo SFM technique. The proposed method is based on the stereo SFM presented in our former paper. The method do not need stereo correspondence but, nevertheless, can determine absolute scale factor, which is an important factor for long term navigation and SLAM. The method use only motion correspondence basically, which is easier to solve than stereo correspondence because the SFM algorithm can use a sequence of images taken at short time intervals. However, we infer the stereo correspondence inversely from the absolute estimates of structure and motion parameters and utilize this information to improve the performance of our method. Consequently, the method maintain the robustness to the stereo correspondence ambiguity and can avoid the degenerate configuration reported in the former paper. We also propose a simple initialization technique for the proposed method based on extended Kalman filter, which is critical issue for the methods using bearing-only measurements. The experimental results demonstrate the effectiveness of the algorithm.

Recursive estimation of absolute stereo motion and structure without stereo correspondence

The stereo SFM combining stereo and motion is a useful vision cue to estimate both the motion of the cameras and the structure of a scene with an absolute scale factor. Most of the works related to the stereo SFM have used both motion correspondence and stereo correspondence. Because it is information redundancy to use these two correspondence simultaneously, they can give accurate results. However, the methods to solve the stereo correspondence problem are not yet reliable enough to have been used in practical applications, while the motion correspondence problem is easy to solve. Therefore, if an absolute scale factor can be acquired by only using motion correspondence without stereo correspondence, it is not necessary to use stereo correspondence for accuracy at the cost of reliability. This paper shows that the scale factor can be determined by using only motion correspondence. Moreover, it is shown that the degenerate cases reported in this paper are all the cases and can be easily avoided. This fact is verified from the two theorems and the simulation results. This paper also describes an implementation of the proposed algorithm. This implementation uses multiple images acquired continuously when a stereo camera moves in a scene and is formulated to be a recursive form for real-time applications. Some peculiar implementation techniques contribute to fast convergence rate and good stability in spite of large initial uncertainty. The simulation and experimental results demonstrate the effectiveness of the algorithm.

Camera Motion Tracking using Background Geometry

To composite computer graphic objects and photo-real sequences we should know the camera intrinsic and extrinsic parameters. Many algorithms have been proposed for camera auto-calibration but most of them severely depend on reliable feature tracking so they cannot be applied to moving background sequences like the ocean scene. We assume that intrinsic parameters are known and propose a new method to estimate camera motion using background geometry. The proposed method can be used only in the ocean background sequence but it shows possibility to develop various approaches to estimate camera motion using previous knowledge about background geometry.

Sequential Motion and Scene Reconstruction from Image Sequences Captured by a Multi-camera System

This paper deals with structure and motion estimation by using image sequences captured by a multi-camera system moving in a scene. Single camera systems with a relatively small eld of view have limited accuracy because of the inherent confusion between translation and rotation. This is also the case for the stereo camera systems if the systems need the intersection of the elds of view for stereo correspondence. The cameras constituting the multi-camera system considered ultimately in this paper are arranged so that there is no, or a small, intersection of the elds of view. This conguration improves the accuracy of the structure and motion estimation because the ambiguity mentioned above decreases due to a large eld of view. We propose a recursive algorithm for real time applications of the multi-camera system using long term image sequences, which have appearing and disappearing point features. The results of the experiments with long term real image sequences are presented to demonstrate the feasibility of the algorithm.