Abstract
The main difficulty to attain fully autonomous robot navigation outdoors is the fast detection of reliable visual references, and their subsequent characterization as landmarks for immediate and unambiguous recognition. Aimed at speed, our strategy has been to track salient regions along image streams by just performing on-line pixel sampling. Persistent regions are considered good candidates for landmarks, which are then characterized by a set of subregions with given color and normalized shape. They are stored in a database for posterior recognition during the navigation process. Some experimental results showing landmark-based navigation of the legged robot Lauron III in an outdoor setting are provided.
Keywords
- Color Space
- Autonomous Robot
- Salient Region
- Input Pixel
- Landmark Detection
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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References
DeSouza, G., Kak, A.: Vision for mobile robot navigation: a survey. PAMI 24(2), 237–267 (2002)
Lowe, D.: Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Sala, P., Sim, R., Shokoufandeh, A., Dickinson, S.: Landmark Selection for Vision-Based Navigation. IEEE Trans. on Robotics 22(2), 334–349 (2006)
Murrieta-Cid, R., Parra, C., Devy, M.: Visual navigation in natural environments: from range and color data to a landmark-based model. Autonomous Robots 13(2), 143–168 (2002)
Todt, E., Torras, C.: Detecting Salient Cues Through Illumination-Invariant Color Ratios. Robotics and Autonomous Systems 48(2-3), 111–130 (2004)
Celaya, E., Albarral, J-L., Jiménez, P., Torras, C.: Visually-Guided Robot Navigation: From Artificial To Natural Landmarks. In: 6th International Conference on Field and Service Robotics, Chamonix, France (July 2007)
Busquets, D., Sierra, C., de Màntaras, R.L.: A multi-agent approach to qualitative landmark-based navigation. Autonomous Robots 15, 129–153 (2003)
Nothdurft, H.-C.: Saliency from Feature Contrast: Additivity Across Dimensions. Vision Research 40, 1183–1201 (2000)
Todt, E., Torras, C.: Detection of natural landmarks through multiscale opponent features. In: 15th International Conference on Pattern Recognition, Barcelona, Spain, pp. 976–979 (2000)
Itti, L., Koch, C., Niebur, E.: A Model of Saliency-based visual Attention for Rapid Scene Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
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© 2007 Springer-Verlag Berlin Heidelberg
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Celaya, E., Albarral, JL., Jiménez, P., Torras, C. (2007). Natural Landmark Detection for Visually-Guided Robot Navigation. In: Basili, R., Pazienza, M.T. (eds) AI*IA 2007: Artificial Intelligence and Human-Oriented Computing. AI*IA 2007. Lecture Notes in Computer Science(), vol 4733. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74782-6_48
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DOI: https://doi.org/10.1007/978-3-540-74782-6_48
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74781-9
Online ISBN: 978-3-540-74782-6
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