Sensing of Real Environment by Stereo Omnidirectional System (SOS)

Yamamoto, Kazuhiko; Tanahashi, Hideki; Niwa, Yasumasa; Kuwashima, Shigesumi

doi:10.1541/ieejeiss1987.121.5_876

Cited by 11 publications

(4 citation statements)

References 5 publications

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“…The SOS, which consists of 20 stereo units mounted on the faces of a regular icosahedron, can simultaneously acquire high-resolution color images of the environment and stereo information with a 360° × 180° field of view with a uniform spatial resolution in real time [8,9]. In the current research, we used omnidirectional images with a 360° × ±45° field of view, which were generated from the omnidirectional images of the SOS, to verify the proposed technique.…”

Section: Resultsmentioning

confidence: 99%

“…Hua and Ahuja [7] also obtained high-resolution images of the entire space by using a double (upper and lower) hexagonal pyramid mirror omnidirectional system. We proposed a stereo omnidirec-tional system (SOS) that can simultaneously obtain highresolution color images of the environment and stereo information with a 360° × 180° field of view and uniformity in real time [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Location and pose estimation for active vision using panoramic edge histograms

Wang¹,

Tanahashi

Satoh

et al. 2004

Systems & Computers in Japan

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SUMMARYIn this paper, the authors propose a new technique for robust location and pose estimation for an active vision sensor using images obtained by a camera system (omnidirectional sensor) that can acquire panoramic information of the environment. The proposed technique first calculates a panoramic edge histogram in advance from images obtained by the omnidirectional sensor at several reference points in the environment. To estimate the sensor location and pose, dynamic programming (DP) is extended to twopass DP, and the current location is matched with the panoramic edge histogram at the reference points to obtain the edge shift magnitude at each direction angle. These shift magnitudes have the same periodicity as a sine curve, the sensor rotation corresponds to the offset of the sine curve, and its movement direction corresponds to the 0-phase of the sine curve. In the current research, the sensor's movement direction and rotation angle are efficiently estimated by independently fitting the offset and the 0-phase of its period from these edge shift magnitudes. Finally, the exact location is calculated from the movement directions for the two closest reference points. The effectiveness of the proposed technique is shown by experiments in an actual environment.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Location and pose estimation for active vision using panoramic edge histograms

Wang¹,

Tanahashi

Satoh

et al. 2004

Systems & Computers in Japan

Self Cite

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“…The SOS is a system that can acquire in real time color images and depth images in all directions centered on the system and without blind spots [17]. Figure 1 shows an external view of the system.…”

Section: The Stereo Omnidirectional System (Sos)mentioning

confidence: 99%

“…We have previously proposed a stereo omnidirectional system (SOS) [17]. The SOS, a multicamera system based on a three-eye stereo unit, obtains in real time highresolution color and depth images in all directions centered on the sensor.…”

Section: Introductionmentioning

confidence: 99%

Detection of moving object by mobile stereo omnidirectional system (SOS)

Shimizu

Yamamoto

Wang

et al. 2005

Electrical Engineering Japan

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SUMMARYMoving object detection with a mobile image sensor is an important task in robotics and computer vision, when considering the practical use of robotics in human environments. In this paper, we propose a robust method that detects moving objects in the environment using the omnidirectional depth information obtained by a mobile Stereo Omnidirectional System (SOS). In order to detect only the moving objects within the depth image that are obtained by a sensor in motion, we first estimate the ego-motion of the sensor, and generate a predicted depth image for the current time from the depth obtained at the previous time by only considering the ego-motion of the sensor. Then the predicted depth image is compared with the actual one obtained at the current time, and the inconsistent regions are detected as moving objects. When the sensor moves, occlusions will occur in the scene and they will cause false detections. However, these false detections can be suppressed by estimating the occlusion regions using the egomotion parameters of the sensor and the jump edges in the depth image.

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An image conversion algorithm from fish eye image to perspective image for human eyes

Ishii

Sudo

Hashimoto

Proceedings 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM 2003)

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Sensing of Real Environment by Stereo Omnidirectional System (SOS)

Cited by 11 publications

References 5 publications

Location and pose estimation for active vision using panoramic edge histograms

Location and pose estimation for active vision using panoramic edge histograms

Detection of moving object by mobile stereo omnidirectional system (SOS)

An image conversion algorithm from fish eye image to perspective image for human eyes

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