Moon-Hong Baeg

An object recognition system for a smart home environment on the basis of color and texture descriptors

A prototype smart home environment for service robots has been constructed in the research building of Korea Institute of Industrial Technology (KITECH) to demonstrate the feasibility of a robot-assisted future home environment. An inexpensive service robot outfitted with a camera, a Radio Frequency Identification (RFID) reader, and a communication module is installed in the building as a future service robot system which is cheap but robust. In our robotic system, the RFID reader gets the rough location data and object information, and then the robot performs the object recognition scheme to get the exact position of the object in order to grasp it. Due to its concise and unambiguous description of the complex media contents, we adopted MPEG-7 visual descriptors for our object recognition system. In this paper, we propose a fast object recognition system for our smart home environment project, on the basis of MPEG-7 visual descriptors including color and texture. Experimental results show that our proposed system works well with good performance in terms of speed and recognition rate. This object recognition system will be incorporated into our mobile robotics platform and a shape descriptor is to be included in the next version of our object recognition system.

Pose estimation of quadratic surface using surface fitting technique

A key problem in robotics is the estimation of the location and orientation of objects from surface measurement data. This is termed pose estimation. A fundamental task is the pose estimation of known quadratic surfaces from, possibly noisy, data. A solution for this task facilitates pose estimation for more complex objects. Current algorithms frequently converge to local minima of the performance index and/or pay a high computing cost and/or are sensitive to noise, that are unsuited for online applications because of the intensive computer effort required. The goal is to develop a fast and robust algorithm for pose estimation using range data. Here, pose estimation is carried out using algebraic techniques in a two stage optimization procedure involving least squares estimation, or better the method of instrumental variables, and 3/spl times/3 matrix diagonalizations. The procedure leads to zero pose estimation error in the noise free finite data case, and in the case of infinite data with additive white noise.

Real-time Polygon Generation and Texture Mapping for Tele-operation using 3D Point Cloud Data

In this paper, real-time polygon generation algorithm of 3D point cloud data and texture mapping for tele-operation is proposed. In a tele-operation, it is essential to provide more highly realistic visual information to a tele-operator. By using 3D point cloud data, the tele-operator can observe the working environment from various view point with a reconstructed 3D environment. However, there are huge empty space in 3D point cloud data, since there is no environmental information among the points. This empty space is not suitable for an environmental information. Therefore, real-time polygon generation algorithm of 3D point cloud data and texture mapping is presented to provide more highly realistic visual information to the tele-operator. The 3D environment reconstructed from the 3D point cloud data with texture mapped polygons is the crucial part of the tele-operation.

π-SIFT: A photometric and Scale Invariant Feature Transform

For many years, various local descriptors that are insensitive to geometric changes such as viewpoint, rotation, and scale changes, have been attracting attention due to their promising performance. However, most existing local descriptors including the SIFT (Scale Invariant Feature Transform) are based on luminance information rather than color information thereby resulting in instability to photometric variations such as shadows, highlights, and illumination changes. In this paper, we propose a novel local descriptor, π-SIFT, that are invariant to both geometric and photometric variations. In order to achieve photometric invariance, we adopt photometric quasi-invariant features based on the dichromatic reflection model. The performance of the proposed descriptor is evaluated with SIFT.

Visual feedback control for tracking and intercepting 3D moving object

This paper describes the visual feedback control system that can track and intercept a 3D moving object on conveyor belt system. We assume that the 3D object consists of quadratic surfaces. Laser range finder is located on an end-effector of the robot manipulator to obtain 3D range data of the object. We have used the surface segment fitting technique to determine the relative pose of the 3D object and robot. The pose estimation of object is converted to nonlinear optimization problem to minimize an error between the measured surface patch and the surface from the CAD model. We have performed computer simulations and experiments using integrated robot-conveyor and vision system. It demonstrates that our algorithm can track and intercept the 3D object moving on a conveyor belt.<<ETX>>