. Top 20 SIFT feature patches by histogram count from the bagof-words model for each location denoted by the measurement number for an experiment. Only every second measurement is shown. The measurements corresponding to landmarks (i.e. where the landmark detector fires) are shown in red (shaded overlay). It can be seen that these correspond to the start of subsequences of measurements that also differ qualitatively from the preceding measurements, for example measurements before 34 are much more cluttered than those following it. Abstract-Automatic detection of landmarks, usually special places in the environment such as gateways, for topological mapping has proven to be a difficult task. We present the use of Bayesian surprise, introduced in computer vision, for landmark detection. Further, we provide a novel hierarchical, graphical model for the appearance of a place and use this model to perform surprise-based landmark detection. Our scheme is agnostic to the sensor type, and we demonstrate this by implementing a simple laser model for computing surprise. We evaluate our landmark detector using appearance and laser measurements in the context of a topological mapping algorithm, thus demonstrating the practical applicability of the detector.
In REF , Ranganathan et al. use bayesian surprise for identifying salient landmarks for topological mapping with vision and laser features.
Bayesian surprise and landmark detection
2009 IEEE International Conference on Robotics and Automation
2009 IEEE International Conference on Robotics and Automation
Computer Science