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Thanks to the efforts of the robotics and autonomous systems community, robots are becoming ever more capable. There is also an increasing demand from end-users for autonomous service robots that can operate in real environments for extended periods. In the STRANDS project we are tackling this demand head-on by integrating state-of-the-art artificial intelligence and robotics research into mobile service robots, and deploying these systems for long-term installations in security and care environments. Over four deployments, our robots have been operational for a combined duration of 104 days autonomously performing end-user defined tasks, covering 116km in the process. In this article we describe the approach we have used to enable long-term autonomous operation in everyday environments, and how our robots are able to use their long run times to improve their own performance

The "Point Distribution Model", derived by analysing the modes of variation of a set of training examples, can be a useful tool in machine vision. One of the drawbacks of this approach to date is that the training data is acquired with human intervention where fixed points must be selected by eye from example images. A method is described for generating a similar flexible shape model automatically from real image data. A cubic B-spline is used as the shape vector for training the model. Large training sets are used to generate a robust model of the human profile for use in the labelling and tracking of pedestrians in real-world scenes. Furthermore, an extended model is described which incorporates direction of motion, allowing the extrapolation of direction from shape.

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