Seung-Ho Baeg

Building a smart home environment for service robots based on RFID and sensor networks

This paper is concerned with constructing a prototype smart home environment which has been built in the research building of Korea Institute of Industrial Technology (KITECH) to demonstrate the practicability of a robot-assisted future home environment. Key functionalities that a home service robot must provide are localization, navigation, object recognition and object handling. A considerable amount of research has been conducted to make the service robot perform these operations with its own sensors, actuators and a knowledge database. With all heavy sensors, actuators and a database, the robot could have performed the given tasks in a limited environment or showed the limited capabilities in a natural environment. We initiated a smart home environment project for light-weight service robots to provide reliable services by interacting with the environment through the wireless sensor networks. This environment consists of the following three main components: smart objects with an radio frequency identification (RFID) tag and smart appliances with sensor network functionality; the home server that connects smart devices as well as maintains information for reliable services; and the service robots that perform tasks in collaboration with the environment. In this paper, we introduce various types of smart devices which are developed for assisting the robot by providing sensing and actuation, and present our approach on the integration of these devices to construct the smart home environment. Finally, we discuss the future directions of our project.

RoboMaidHome: A Sensor Network-based Smart Home Environment for Service Robots

Over the past few years, many research groups have attempted to build smart environments. Recently, several groups have been actively conducting research into the construction of smart home environment for service robots to be used as assistants. A home service robot must have are localization, navigation, object recognition, and object handling functionalities. These operations are usually performed by a service robot by itself and the robot can perform the given tasks in a limited environment. In addition, they have shown only limited capabilities in a natural environment. We initiated a smart home environment project, RoboMaidHome, for light-weight service robots to provide reliable services by interacting with the environment through wireless sensor networks. This environment consists of the following three main components: (i) smart objects and smart appliances with sensor network functionality; (ii) a home server that connects smart devices as well as maintains information for reliable services; and (iii) service robots that perform tasks in collaboration with the environment. In this paper, we explain the basic concepts and the architecture of our project and address some key issues related to the project. Finally, we discuss the future directions of our project.

Drift Reduction in Pedestrian Navigation System by Exploiting Motion Constraints and Magnetic Field

Pedestrian navigation systems (PNS) using foot-mounted MEMS inertial sensors use zero-velocity updates (ZUPTs) to reduce drift in navigation solutions and estimate inertial sensor errors. However, it is well known that ZUPTs cannot reduce all errors, especially as heading error is not observable. Hence, the position estimates tend to drift and even cyclic ZUPTs are applied in updated steps of the Extended Kalman Filter (EKF). This urges the use of other motion constraints for pedestrian gait and any other valuable heading reduction information that is available. In this paper, we exploit two more motion constraints scenarios of pedestrian gait: (1) walking along straight paths; (2) standing still for a long time. It is observed that these motion constraints (called “virtual sensor”), though considerably reducing drift in PNS, still need an absolute heading reference. One common absolute heading estimation sensor is the magnetometer, which senses the Earth’s magnetic field and, hence, the true heading angle can be calculated. However, magnetometers are susceptible to magnetic distortions, especially in indoor environments. In this work, an algorithm, called magnetic anomaly detection (MAD) and compensation is designed by incorporating only healthy magnetometer data in the EKF updating step, to reduce drift in zero-velocity updated INS. Experiments are conducted in GPS-denied and magnetically distorted environments to validate the proposed algorithms.

A new object recognition system for service robots in the smart environment

This paper describes a new object recognition system for service robots in a smart environment full of RFID tags and connected by wireless networks. Object recognition is one of the basic functionalities a service robot should perform and many researchers have attempted to make the service robot recognize objects through vision processing in natural environments. However there is no conventional vision system that can recognize target objects robustly in a real-world workspace. In our smart environment, flexibility and robustness in object recognition are provided with the help of RFID tags and communication networks. RFID tags provide the presence and identity information of the object and the robot can recognize the object through vision processing based on a detected RFID code and downloaded visual descriptor information. For feature descriptors, we adopted MPEG-7 visual descriptors due to its concise and unambiguous description of the complex media contents. This paper focuses on developing our object recognition engine based on visual descriptors information in the smart environment. To this end, we propose a new object recognition architecture for service robots, and present a implemented matching algorithm on the basis of MPEG-7 visual descriptors such as color and texture. Experimental results show that the proposed system works well with good performance in terms of recognition rate. This object recognition system will be incorporated into our service robot platform and a pose estimation module is to be included in the next version of our object recognition system.

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.

Reliable feature point detection and object pose estimation using photometric quasi-invariant SIFT

Object pose estimation from stereo images with unknown correspondence is a thoroughly studied problem in the computer vision and robot engineering literatures. Especially, it is important to detect the desirable corresponding points from images for object pose estimation. For this, many approaches have been proposed. Among them, the local feature descriptor, which describe the feature points that are robust to image deformations in an object or image, is one of the most promising approaches that has been applied to the stable feature detection problem successfully. Although any descriptors including the SIFT represent superior performance, these are based on luminance information rather than color information thereby resulting in instability to photometric variations such as shadows, highlights, and illumination changes. Therefore, we propose a novel method which extracts the interest points that are insensitive to both geometric and photometric variations in order to estimate more accurate and desirable object pose. In this method, we use photometric quasi-invariant features based on the dichromatic reflection model in order to achieve photometric invariance, and the SIFT is used for geometric invariance as well. The performance of the proposed method is evaluated with other local descriptors. Experimental results show that our method gives similar performance or outperforms them with respect to various imaging conditions. Finally, we estimate object pose by using the features extracted via the proposed method.

Advanced compact 3D lidar using a high speed fiber coupled pulsed laser diode and a high accuracy timing discrimination readout circuit

At last year, we have been developing 3D scanning LIDAR designated as KIDAR-B25, which features the 3D scanning structure based on an optically and mechanically coupled instrument. In contrast with previous scanning LIDARs, vertical scanning is realized using two stepping motors synchronized with movement and moves in a spiral. From the results of outdoor experiments conducted last year to evaluate and measure the LIDAR performance and stability, we identified some limitations and problems that should be resolved. In the first instance, the samples per second are inefficient for use in detection, object clustering, and classification. In addition, the accuracy and precision of distance at every point is seriously affected by the reflectance and distance of the target. Therefore, we have focused on improving the 3D LIDAR range finding performance, speed of measurement, and stability regardless of environmental variation. Toward the realization of these goals, in this paper, we deal with two improvements compared with previous 3D LIDAR.

An intelligent navigation method for service robots in the smart environment

Autonomous navigation is one of primitive functionalities which service robots should have; nevertheless, the navigation problem for a service robot still has many difficulties because the real environment where service robots should works is so complex and dynamical. This paper proposes a framework of intelligent navigation for service robots based on a semantic map of the smart environment. In the smart environment, the robot can receive his position information from location sensors by sensor networks, and it can eliminate the accumulated localization errors. So the robot can accomplish confidential localization in the dynamic and complex environment. With the topological information in the semantic map, path planning problem can be simple even in the wide and complex spaces, and topological path from semantic map can be divided into several sub goals. Robust navigation can be accomplished by moving towards these sub goals with reactive navigation algorithm which has robust characteristics in the dynamic environments. Our approaches have ascertained the good performance on the localization and the navigation, and the feasibility of these methods could be confirmed with the result of experiments in the real environment.

Development of a Real Time Locating System Using PSD under Indoor Environments

This paper considers a new positioning sensor using position sensitive detector (PSD) under indoor environments. The system consists of PSD module which is attached on the ceiling and infrared (IR) projector modules attached on the interested object, and all modules are connected by wireless communication. In this system, the PSD module detects and determines location of the IR projector module with its light source, and the IR projector module receives its own location information from the PSD module. In particular, the developed system is implemented on the embedded system and it is small, simple, inexpensive and robust localization system. We applied this system to mobile robot navigation on indoor environment, and the experimental results shows the effectiveness of this system

Drift reduction in IMU-only pedestrian navigation system in unstructured environment

This paper aims at developing algorithmic approach to reduce the error drift in navigation information using only single motion sensor i.e. inertial measurement unit (IMU) in an unstructured environment. The target application is pedestrian navigation system (PNS), but these algorithms are general for other applications too involving autonomous systems. A MEMS IMU is mounted on the foot of the agent in order to get benefit of known walking patterns of the wearer. Algorithms are derived based on motion constraints of human walking and applied to reduce the inherent error drift in inertial navigation systems (INS). Experiments are conducted in indoor/outdoor unstructured environment to validate this algorithmic approach and shown that without using any other sensors and any other pre-conditions on the environment; we can reduce navigation errors significantly only by taking into account the motion constraints of human walking.