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This paper presents a novel method for estimating the heading angle of a home service robot. The images captured from a forward viewing mono-camera, gyroscope data, and wheel encoder data are used as sensory inputs. The data rates of camera, gyroscope, and encoder are 1 Hz, 50 Hz, and 50 Hz, respectively. The camera is slightly tilted by 16.5° to increase the amount of visual information obtained. The context of this study is simultaneous localization and mapping (SLAM) in an indoor home environment. The ceiling lines, i.e., the intersection between the ceiling and wall, are used as the only landmarks for the heading angle estimation. In our method, since the ceiling lines are used as the sole landmarks, line parameterization and data association problem can be simplified when compared to the general line based methods. In home environment, the ceiling line features can be easily detected regardless of the robot rotation. The developed method can be used to accurately estimate the global heading angle while the robot is in motion. A ceiling line map is also reconstructed. The reconstructed ceiling line map provides intuitively understandable spatial information about the environment using only a few features. The experimental results demonstrate that it is feasible to use the proposed method for an indoor home environment.

This paper presents the performances of a corner detector and a region detector in vision-based Simultaneous Localization and Mapping (SLAM). In vision-based SLAM, a feature detection process for data-association is the first step. Therefore, the repeatability of a feature detector is important. Feature detectors are generally categorized as the region detector and the corner detector. There are conflicting research results for the repeatability of corner detectors and region detectors. This paper shows SLAM results using a corner detector and a region detector. Extended Kalman Filters (EKF) are used for implementing the two SLAM methods. The localization errors are evaluated quantitatively. From the experimental results, it is demonstrated that the corner detector is more efficient than the region detector for the work performed in this paper.

This paper presents a new localization method by the data fusion of vision sensor and motion sensor measurements, which can be used for mobile robots. The developed system estimates the orientation, position and velocity of a mobile robot. This is achieved by using data from a camera, robot wheel encoders, an accelerometer, and a gyroscope. The developed system is a multi-rate sampled data system. For the accurate estimation of robot position and velocity, the developed method detects the slip of robot wheels, by comparing the data from the encoders and the accelerometer. The developed method estimates the robot orientation and position by using an extended Kalman filter. The experiments are performed to verify the localization performance of the developed method in several motion scenarios, including a normal operation mode, a wheel slip mode, and a kidnap mode.

This paper presents several different sensor-fusion methods for vision tracking, to keep a camera's line of sight on a target. The data-fusion methods in this paper use several combinations of vision and motion information. The vision information is acquired through a vision processing ASIC (Application-Specific Integrated Circuit), which has an ODR (Output Data Rate) of 27 Hz. The motion information is acquired through one or more sensors, comprising a 3-axis accelerometer, a 3-axis gyroscope, and robot wheel encoders, which have the ODR of 100Hz, 100Hz, and 50 Hz, respectively. Several data-fusion scenarios are tested with these different sensors. An extended kalman filter and an appropriate data-fusion method are applied to each method. Also tested for comparison is a vision-only scenario with a slower ODR of 6.75 Hz. The vision-only method with the fast ODR of 27 Hz performs reasonably well. However, the slower case with the ODR of 6.75 Hz does not provide an adequate performance. The sensor-fusion method that uses all three motion sensors and a vision sensor greatly improves the target tracking performance, regardless of the vision sensor ODR. The motion sensors used are very low cost mass production sensors, found commonly in mobile phones. The proposed method presents a viable solution for target tracking in mobile robots.

We propose a system for enhancing a human-robot interaction system. The goal is to embrace surrounding persons into a conversation with the robot by means of establishing or keeping eye contact. For this, a simple and hence computationally efficient people tracking algorithm has been developed, which in itself is an enhancement of existing approaches. As the foundation of our approach we use a vanilla Kalman filter that explicitly models the velocity in the state space, while the observations are transformed into a unified global coordinate system. We evaluate different integration strategies for multiple sensor cues. Furthermore, we extend an algorithm for group detection to be able to recognize individuals.

This paper presents a vision-tracking system for mobile robots, which travel in a 3-dimentional environment. The developed system controls pan and tilt actuators attached to a camera so that a target is always directly in the line of sight of the camera. This is achieved by using data from robot wheel encoders, a 3-axis accelerometer, a 3-axis gyroscope, pan and tilt motor encoders, and camera. The developed system is a multi-rate sampled data system, where the sampling rate of the camera is different with that of the other sensors. For the accurate estimation of the robot velocity, the developed system detects the slip of robot wheels, by comparing the data from the encoders and the accelerometer. The developed system estimates the target position by using an extended Kalman filter. The experiments are performed to show the tracking performance of the developed system in several motion scenarios, including climbing slopes and slip cases.