Ki-Wan Choi

Improvement of dead reckoning accuracy of a mobile robot by slip detection and compensation using multiple model approach

Although dead reckoning based on odometry and inertial sensors is essential for a robotic localization system, none of previous works gives reliable and accurate position estimates on irregular terrain over long periods of time. Classical approaches use one estimator (such as a Kalman filter) with a single system model. However the single system model is not good to deal with both of slip and no-slip situations because of the dynamics changes. In this paper, a multiple model approach that uses two Kalman filters is presented: one Kalman filter accounting for no-slip condition and the other for slip condition. The Interacting Multiple Model (IMM) is adopted to switch two Kalman filters depending on whether slip occurs or not, and gives the weighted sum of two filter estimates. Experimental results are included to validate our approach.

Constrained Kalman Filter for Mobile Robot Localization with Gyroscope

The odometry information used in localization can be quite erroneous when the robot follows the curved path or suffers from slippage. Thus the use of the low-cost gyroscope to compensate for an angular error is considered by many researchers. Conventional Kalman filtering methods that fuse the odometry with the gyroscope may produce infeasible solution because the robot parameters are estimated regardless of their physical constraints. In this paper, we propose a constrained Kalman filtering method that applies general constrained optimization technique to the estimation of the robot parameters. The state observability is improved by the additional state variables and the accuracy is also improved by the nonapproximated Kalman filter design. Experimental results show the proposed method effectively compensates for the odometry error and yields feasible parameter estimation at the same time

Monocular SLAM with undelayed initialization for an indoor robot

This paper presents a new feature initialization method for monocular EKF SLAM (Extended Kalman Filter Simultaneous Localization and Mapping) which utilizes a 3D measurement model in the camera frame rather than 2D pixel coordinates in the image plane. The key idea is to regard a camera as a range and bearing sensor, of which the range information contains numerous uncertainties. 2D pixel coordinates of measurement are converted to 3D points in the camera frame with an assumed depth. The element of the measurement noise covariance corresponding to the depth of the feature is set to a very high value. And it is shown that the proposed measurement model has very little linearization error, which can be critical for the EKF performance. Furthermore, this paper proposes an EKF SLAM system that combines odometry, a low-cost gyro, and low frame rate (1-2 Hz) monocular vision. Low frame rate is crucial for reducing the price of the processor. This system combination is cost-effective enough to be commercialized for a real vacuum cleaning application. Simulations and experimental results show the efficacy of the proposed method with computational efficiency in indoor environments.

Fuzzy-logic-assisted interacting multiple model (FLAIMM) for mobile robot localization

Improvement of dead reckoning accuracy is essential for robotic localization systems and has been intensively studied. However, existing solutions cannot provide accurate positioning when a robot suffers from changing dynamics such as wheel slip. In this paper, we propose a fuzzy-logic-assisted interacting multiple model (FLAIMM) framework to detect and compensate for wheel slip. Firstly, two different types of extended Kalman filter (EKF) are designed to consider both no-slip and slip dynamics of mobile robots. Then a fuzzy inference system (FIS) model for slip estimation is constructed using an adaptive neuro-fuzzy inference system (ANFIS). The trained model is utilized along with the two EKFs in the FLAIMM framework. The approach is evaluated using real data sets acquired with a robot driving in an indoor environment. The experimental results show that our approach improves position accuracy and works better in slip detection and compensation compared to the conventional multiple model approach.

Motion Compensation for Ultrasound Thermal Imaging Using Motion-Mapped Reference Model: An in vivo Mouse Study

Ultrasound (US)-based thermal imaging is very sensitive to tissue motion, which is a major obstacle to apply US temperature monitoring to noninvasive thermal therapies of in vivo subjects. In this study, we aim to develop a motion compensation method for stable US thermal imaging in in vivo subjects. Based on the assumption that the major tissue motion is approximately periodic caused by respiration, we propose a motion compensation method for change in backscattered energy (CBE) with multiple reference frames. Among the reference frames, the most similar reference to the current frame is selected to subtract the respiratory-induced motions. Since exhaustive reference searching in all stored reference frames can impede real-time thermal imaging, we improve the reference searching by using a motion-mapped reference model. We tested our method in six tumor-bearing mice with high intensity focused ultrasound (HIFU) sonication in the tumor volume until the temperature had increased by 7°C. The proposed motion compensation was evaluated by root-meansquare-error (RMSE) analysis between the estimated temperature by CBE and the measured temperature by thermocouple. As a result, the mean±SD RMSE in the heating range was 1.1 ± 0.1°C with the proposed method, while the corresponding result without motion compensation was 4.3 ± 2.6°C. In addition, with the idea of motion-mapped reference frame, total processing time to produce a frame of thermal image was reduced in comparison with the exhaustive reference searching, which enabled the motioncompensated thermal imaging in 15 frames per second with 150 reference frames under 50% HIFU duty ratio.

Heart Rate Monitor for Portable MP3 Player

This paper presents a photoplethysmography sensor based on a heart rate monitor for a portable MP3 player. Two major design issues are addressed: one is to acquire the sensor signal with a proper amplitude despite a wide range of variation and the other is to handle the noise contaminated signal which is caused by a motion artifact. A benchmarking test with a professional medical photoplethysmography sensor shows that our device performs very well in calculating heart rate even though our photoplethysmography sensor module was designed to be cost effective

Method of Measuring Calorie Consumption for Portable Apparatus

A measuring device for use in measuring calorie consumption includes a pulse input unit which detects a first heart rate of the user at a first point in an exercise period and a second heart rate of the user at a second point of the exercise period other than the first point, the pulse input unit not detecting a third heart rate between the first and second heart rates; and a controller which receives the detected first and second heart rates and calculates calories consumed using the detected first heart rate, the detected second heart rate, an at rest heart rate of the user, and one or more of an age, gender, weight, height and an at rest heart rate of the user. The measuring device is usable in a portable device, such as a portable digital audio and/or video reproducing apparatus

Mobile Robot Localization Using a Gyroscope and Constrained Kalman Filter

Recently, cleaning robots are gaining popularity for saving time and household labor. To clean the room effectively, the robot should have localization capability. The odometry information used in low-cost localization can be quite erroneous when the robot suffers from slippage. Thus the use of the low-cost MEMS gyroscope to compensate for an angular error is considered by many researchers. Conventional Kalman filtering method that fuses the odometry with the gyroscope may produce infeasible solution because the robot parameters are estimated regardless of their physical constraints. In this paper, we propose a constrained Kalman filtering method that applies general constrained optimization technique to the estimation of the robot parameters. The state observability is improved by the additional state variables and the accuracy is also improved by the non-approximated Kalman filter design. Experimental results show the proposed method effectively compensates for the odometry error and yields feasible parameter estimation at the same time

A reliability index of shear wave speed measurement for shear wave elastography

Shear wave elastography is widely used for the noninvasive measurement of tissue stiffness. Assessing liver fibrosis, for example, by measuring shear wave propagation speed is gaining popularity. Elastography data, however, is often contaminated with low SNR and the results become unreliable. Quantification of the reliability of shear wave measurement, therefore, has a significant practical importance. We propose a performance index called “Reliability Measurement Index (RMI)” which is calculated by the weighted sum of two factors: the residual of the wave equation and the magnitude of the shear wave. The residual error accounts for the deviation from the wave equation due to error sources including measurement noises and motion artifacts. Magnitude of induced shear wave is also used to reflect the SNR of the signal. The proposed approach has been successfully implemented on a commercial diagnostic ultrasound system (RS-80A with Prestige, Samsung Medison, Korea). The proposed performance index has been tested in both phantom and in-vivo studies. Initial studies show that high values of RMI are strongly correlated with reproducible measurements. The proposed index can be utilized to filter out unreliable measurements and result in performance improvement of shear wave elastography.

Noninvasive ultrasound temperature imaging with fusion algorithm

There is a great demand for a noninvasive temperature imaging to commercialize the US guided HIFU system. Echo shift is currently the most technologically developed method, but it cannot give an exact temperature over roughly 43°C since the relation between temperature and speed of sound is irregular depending on the temperature range and tissue type. CBE (Change in Backscattered Energy) has wider temperature range but has the severe artifact that appears behind heated region on ultrasound beam pathway. The object of this paper was to develop a temperature imaging method over the full temperature range by remedying these shortcomings. We present analysis for characteristics of echo shift and CBE up to 60°C through experiments and a fusion approach probabilistically combining echo shift and CBE for more exact temperature imaging over wider temperature range. They can be modeled based on the previous fusion temperature images and the current temperature images by each method. Then the current fusion temperature image can be obtained by combining the two temperature images with optimal weights calculated from the two error covariance maps. The possibility of this method was verified through in-vitro experiments.