[Show abstract][Hide abstract] ABSTRACT: A simple and successful design for a panoramic stereo system that uses a single camera and multiple mirrors is presented. A wide-baseline stereo enhances the three-dimensional reconstruction accuracy even with a single camera. The feasibility of the system as a practical stereo sensor has been demonstrated with experiments in an indoor environment.
[Show abstract][Hide abstract] ABSTRACT: This paper presents a model of 3D object recognition motivated from the robust properties of human vision system (HVS). The
HVS shows the best efficiency and robustness for an object identification task. The robust properties of the HVS are visual
attention, contrast mechanism, feature binding, multi-resolution, size tuning, and part-based representation. In addition,
bottom-up and top-down information are combined cooperatively. Based on these facts, a plausible computational model integrating
these facts under the Monte Carlo optimization technique was proposed. In this scheme, object recognition is regarded as a
parameter optimization problem. The bottom-up process is used to initialize parameters in a discriminative way; the top-down
process is used to optimize them in a generative way. Experimental results show that the proposed recognition model is feasible
for 3D object identification and pose estimation in visible and infrared band images.
[Show abstract][Hide abstract] ABSTRACT: We present an accurate metric localization method using a simple artificial landmark for the navigation of indoor mobile robots. The proposed landmark model is designed to have a three-dimensional, multi-colored structure and the projective distortion of the structure encodes the distance and heading of the robot with respect to the landmark. Catadioptric vision is adopted for the robust and easier acquisition of the bearing measurements for the landmark. We propose a practical EKF based self-localization method that uses a single artificial landmark and runs in real time.
[Show abstract][Hide abstract] ABSTRACT: Simultaneous localization and mapping is an important task for autonomous mobile robot. To let the robot explore a new environment without any prior map, real-time estimation of the geometrical relation between the robot and the environment is necessary. Extended Kalman filter (EKF)-based approaches are the most common. However, they always have the risk of collapse where the assumption of Gaussian distribution is not applicable. It is well known that state estimation with a particle filter is very robust against clutter in dynamic and noisy environments because of its ability to represent non-Gaussian distributions. Unfortunately, particle-based posterior representation in high dimensions is extremely expensive. We propose an approach, named partitioned recursive SLAM, that overcomes the complexity problem arising in adopting a particle filter in SLAM. By partitioning the state and alternating the turns for the state update, the computational capacity required to process SLAM is reduced to scale linearly with the number of landmarks in the map.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we present a framework for novel catadioptric stereo camera system that uses a single camera and a single lens with conic mirrors. Various possible designs of the catadioptric stereo system with single-viewpoint constraint have been developed in this framework. The proposed systems are compact wide-baseline stereo systems with a panoramic view. The simple structures of the systems reduce the problems of misalignment between the camera and mirrors, which make the system free of complex search procedure for epipolar line. The wide baseline enables accurate 3D-reconstruction of the environment. Additionally, the system has all the advantages of the single camera stereo system that stem from the same physical characteristics of the camera. The feasibility of the system as a practical stereo sensor has been demonstrated with experiments in an indoor environment.
[Show abstract][Hide abstract] ABSTRACT: This paper presents a novel localization paradigm for mobile robots based on artificial and natural landmarks. A model-based object recognition method detects natural landmarks and conducts the global and topological localization. In addition, a metric localization method using artificial landmarks is fused to complement the deficiency of topology map and guide to action behavior. The recognition algorithm uses a modified local Zernike moments and a probabilistic voting method for the robust detection of objects in cluttered indoor environments. An artificial landmark is designed to have a three-dimensional multi-colored structure and the projection distortion of the structure encodes the distance and viewing direction of the robot. We demonstrate the feasibility of the proposed system through real world experiments using a mobile robot KASIRI-III.