@Article{ngo:itis:2010,
  author    = {Ngo, Trung, Thanh and Kojima, Yuichiro and Nagahara, Hajime and Sagawa, Ryusuke and Mukaigawa, Yasuhiro and Yachida, Masahiko and Yagi, Yasushi},
  title     = {Real-time Estimation of Fast Egomotion with Feature Classification using Compound Omnidirectional Vision Sensor},
  journal   = {IEICE Transactions on Information and Systems},
  year      = {2010},
  volume    = {E93-D},
  number    = {01},
  pages     = {152--166},
  month     = {January},
  doi       = {https://doi.org/10.1587/transinf.E93.D.152},
  url       = {https://www.jstage.jst.go.jp/article/transinf/E93.D/1/E93.D\_1\_152/\_pdf/-char/en},
  keywords  = {compound omnidirectional vision, multi-baseline stereo, large FOV, motion parameter separation, fast egomotion estimation, RANSAC},
  abstract  = {For fast egomotion of a camera, computing feature correspondence and motion parameters by global search becomes highly time-consuming. Therefore, the complexity of the estimation needs to be reduced for real-time applications. In this paper, we propose a compound omnidirectional vision sensor and an algorithm for estimating its fast egomotion. The proposed sensor has both multi-baselines and a large field of view (FOV). Our method uses the multi-baseline stereo vision capability to classify feature points as near or far features. After the classification, we can estimate the camera rotation and translation separately by using random sample consensus (RANSAC) to reduce the computational complexity. The large FOV also improves the robustness since the translation and rotation are clearly distinguished. To date, there has been no work on combining multi-baseline stereo with large FOV characteristics for estimation, even though these characteristics are individually are important in improving egomotion estimation. Experiments showed that the proposed method is robust and produces reasonable accuracy in real time for fast motion of the sensor.},
  publisher = {IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG},
  address   = {KIKAI-SHINKO-KAIKAN BLDG MINATO-KU SHIBAKOEN 3 CHOME, TOKYO 105, JAPAN}
}