Abstract
When a world-observing camera moves through a scene capturing images continuously, it is possible to analyse the images to estimate its ego-motion, even if nothing is known in advance about the contents of the scene around it. The key to solving this apparently chicken-and-egg problem is to detect and repeatedly measure a number of salient ‘features’ in the environment as the camera moves. Under the usual assumption that most of these are rigidly related in the world, the many geometric constraints on relative camera/feature locations provided by image measurements allow one to solve simultaneously for both the camera motion and the 3D world positions of the features. While global optimisation algorithms are able to achieve the most accurate solutions to this problem, the consistent theme of my research has been to develop ‘Simultaneous Localisation and Mapping’ (SLAM) algorithms using probabilistic filtering which permit sequential, hard real-time operation.
The author’s recent research has been supported by an EPSRC Advanced Research Fellowship and EPSRC Grants GR/R89080/01 and GR/T24684. He is grateful to his long-term collaborators, particularly Ian Reid, José María Montiel, Nobuyuki Kita, Olivier Stasse, Walterio Mayol and David Murray.
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References
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Davison, A.J. (2007). Vision-Based SLAM in Real-Time. In: Martí, J., Benedí, J.M., Mendonça, A.M., Serrat, J. (eds) Pattern Recognition and Image Analysis. IbPRIA 2007. Lecture Notes in Computer Science, vol 4477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72847-4_3
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