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Mapping with limited sensing

For a mobile robot to interact with its environment in meaningful ways, it must possess a model of the environment, i.e., a map. In most cases it is infeasible for an accurate map to be constructed by hand and provided to the robot prior to deployment. Therefore, the robot must construct a map online. Most mapping algorithms assume the availability of frequent, high fidelity feedback from the environment acquired using sensors such as scanning laser rangefinders. Such sensors provide dense, accurate, and long-range data at great expense: they typically cost thousands of (US) dollars, consume significant power and space resources, and require the use of powerful computers for data processing. These sensors are therefore unsuitable for deployment in many applications, e.g., consumer-level robots, disposable robots for search and rescue or hazardous material detection, or mobile sensor networks. The proposed thesis will develop and demonstrate algorithms for mapping with more limited sensors such as infrared rangefinder arrays, which are inexpensive, low-power, and small. The tradeoff of using such sensors is that they give only low-resolution, short-range feedback about the environment and thus are difficult to use for mapping. The first challenge addressed by the proposed thesis is simply to adapt current mapping techniques to the case of limited sensing. Next, uncertainty in the robot’s state and the map can be very large for robots with restricted sensing capabilities; it is therefore also necessary to develop techniques to manage and reduce uncertainty. The proposed thesis will accomplish this by incorporating prior knowledge in the mapping process, by improving the representation of uncertainty, by integrating exploration strategies into mapping, and by utilizing both structural and geometrical information to build maps. Finally, all of the mapping algorithms developed in the thesis will be validated experimentally through simulation, tests on benchmark datasets, and implementation and deployment on real robots.