This paper describes a precision positioning technique that can be applied to vehicles in urban areas. The proposed technique mitigates Global Positioning System (GPS) multipath by means of an omnidirectional infrared (IR) camera that can eliminate the need for invisible satellites [a satellite detected by the receiver but without line of sight (LOS)] by using IR images. Some simple GPS multipath mitigation techniques, such as the installation of antennas away from buildings and using choke ring antennas, are well known. Further, various correlator techniques can also be employed. However, when a direct signal cannot be received by the antenna, these techniques do not provide satisfactory results because they presume that the antenna chiefly receives direct signals. On the other hand, the proposed technique can mitigate GPS multipath, even if a direct signal cannot be received because it can recognize the surrounding environment by means of an omnidirectional IR camera. With the IR camera, the sky appears distinctively dark; this facilitates the detection of the borderline between the sky and the surrounding buildings, which are captured in white, due to the difference in the atmospheric transmittance rate between visible light and IR rays. Positioning is performed only with visible satellites having fewer multipath errors and without using invisible satellites. With the proposed system, static and kinematic evaluations in which invisible satellites are discriminated through observation using an omnidirectional IR camera are conducted. Hence, signals are received even if satellites are hidden behind buildings; furthermore, the exclusion of satellites having large errors from the positioning computation becomes possible. The evaluation results confirm the effectiveness of the proposed technique and the feasibility of highly accurate positioning.

This paper describes a precision positioning technique that can be applied to vehicles in urban areas. The proposed technique mitigates GPS multipath by means of an omnidirectional infrared (IR) camera that can eliminate the need for invisible satellites (a satellite detected by the receiver but without LOS (Line Of Sight)) by using IR images. Some simple GPS multipath mitigation techniques such as installation of antennas away from buildings and using choke ring antennas are well known. Further, various correlator techniques can also be employed. However, when a direct signal cannot be received by the antenna, these techniques do not provide satisfactory results because they presume that the antenna chiefly receives direct signals. On the other hand, the proposed technique can mitigate GPS multipath even if a direct signal cannot be received because it can recognize the surrounding environment by means of an omnidirectional IR camera. With the IR camera, the sky appears distinctively dark; this facilitates the detection of the borderline between the sky and the surrounding buildings, which are captured in white, due to the difference in the atmospheric transmittance rate between visible light and the IR rays. Positioning is performed only with visible satellites having less multipath errors, and without using invisible satellites. With the proposed system, static and kinematic evaluations in which invisible satellites are discriminated through observation using an omnidirectional IR camera are conducted. Hence, signals are received even if satellites are hidden behind buildings; furthermore, exclusion of satellites having large errors from the positioning computation becomes possible. The evaluation results confirm the effectiveness of the proposed technique and the feasibility of highly accurate positioning.

This study discusses a unique disaster-information collection technique that involves the collaboration of a rescue system and a GIS. The proposed technique can be applied to any rescue system by saving the information collected by the robot as text files. The rescue systems can realize a simple and flexible database across networks by "MISP." Disaster information is collected effectively by different types of rescue robots and transmitted to the "spatial-temporal GIS" using MISP. Effective disaster-information analysis is achieved by the spatial-temporal GIS DiMSIS/DyLUPAs. Field experiments (collapsing house, vast disaster areas) reveal the validity of the proposed disaster-information collection system

This paper describes an autonomous mobile surveillance system usually used in a factory premises with some high-rise buildings. This system consists of a wireless LAN network, a base station and an autonomous vehicle. The vehicle is equipped with a GPS/INS navigation system using the network-based Real-Time Kinematic GPS (RTK-GPS) with Positioning Augmentation Services (PASTM, Mitsubishi Electric Corporation 2003), an Area Laser Radar (ALR), a CCD camera called slaved camera, and an Omni-Directional Vision (ODV) sensor for surveillance and reconnaissance. The vehicle switches navigation modes according to the vehicle status. It has three guidance modes, which are “Normal”, “Road tracking”, and “Crossing recognition”. A field test result shows that the vehicle can track the planned path within 0.10[m] accuracy at straight paths and within 0.25[m] for curved paths even if RTK fixed solutions are not available. Field experiments and analyses have proved that the proposed navigation method can provide good enough guidance accuracy even under poor satellite visibility and that the panorama image database with absolute position is useful for surveillance.

This paper presents a self-positioning system for the mobile robot. The proposed positioning system consists of a stereo setup of two ODVs (omni-directional vision) for obtaining a panoramic image and a disparity image. In general, images obtained by such a device have a relatively low spatial resolution and have some aberration in comparison to standard camera images and thus, considerable range estimation errors are caused. We propose a stereo set up of an ODV, which features two mirrors to minimize blurring influence. We also propose an edge-based intensity interpolation method, a bidirectional sub-pixel matching procedure, and a discontinuity compensation algorithm to reduce disparity error. The relative self-position and the heading angle can be estimated from the direction and the distance toward two landmarks. Comparison between the theoretical positioning accuracy based on the Cramer-Rao lower band and the experimental result in the field prove that both the ODV, the proposed stereo matching algorithm and the self-positioning algorithm are valid and effective.

This paper presents the estimation of depth based on the cylindrical re-projections of images captured by a stereo setup consisting of two sets of an ODV (omnidirectional vision system). In general, images obtained by such a device have a relatively low spatial resolution and have some aberration in comparison to standard camera images and thus, considerable range estimation errors are caused. We propose a stereo setup of an ODV, which features two mirrors with a contrived curvature to minimize blurring influence. We also propose an edge-based intensity interpolation method, a bi-directional sub-pixel matching procedure, and a discontinuity compensation algorithm to reduce the disparity error.

This paper presents a self-positioning system for a mobile robot. The proposed positioning system consists of an omnidirectional vision system (ODV) featuring two mirrors with a contrived curvature for obtaining a panoramic image with low aberration. An obtained panoramic image which occupies most of the visual field can be processed to recognize the topological position of three landmarks which are placed in the environment. A primary marker and two supplementary markers are used to indicate the correspondence between the real environment and the digital map. The relative self-position can be estimated from three directional angles toward landmarks. The absolute self-position can also be computed by comparing the landmark position on a map. Comparison between the theoretical positioning accuracy based on the Cramer-Rao lower bound and the experimental result in the field proves that both the ODV and proposed self-positioning algorithm including subpixel level image processing are valid and effective. An outdoor experiment which used a mobile robot shows that the proposed positioning system have high positioning accuracy even in the cluttered outdoor environment.

This paper presents an autonomous mobile system in an outdoor environment especially applied to a construction site. The system consists of an autonomous vehicle and signs. The autonomous vehicle recognizes instructions given in the signs using a vision system, then it moves or handles materials according to the instruction. The system is adaptable to the changeable environment of the construction site because transportation paths can be easily changed by altering the contents or placement of the signs. Experimental results obtained in the outdoor environment show feasibility of the system.