Where do robots get their information? For a given task, what information is actuallynecessary? What is even meant by "information"? These questions lie at the heart of roboticsand fall under the realm of sensing and filtering.In Sensing and Filtering, the author presents an unusual view of these subjects bycharacterizing the uncertainty due to the many-to-one mappings between the world andsensor readings. This is independent of noise-based uncertainty and reveals critical structureabout the possible problems that can be solved using specific sensors. The set of all sensormodels is arranged into a lattice that enables them to be compared for purposes ofinterchangeability. Filters, which combine sensor observations, are expressed in terms ofinformation states (not information theory), a concept that was introduced in decision andcontrol theory.Sensing and Filtering provides the reader with modeling tools and concepts for developingrobotic systems that accomplish their tasks while carefully avoiding the reconstruction ofunnecessary state information. This is in contrast to the approach usually taken in planningand control, which is to fully reconstruct and maintain the state at all times. The new approachmay enable simple, robust, and inexpensive solutions to tasks such as navigation, topologicalmapping, coverage, patrolling, tracking, and pursuit-evasion.