The is stored efficiently, both in memory and on disk. This also allows for efficient visualizations which scale from coarse overviews to detailed close-up views. The map is multi-resolution so that, for instance, a high-level planner is able to use a coarse map, while a local planner may operate using a fine resolution. Instead, the map is dynamically expanded as needed. The extent of the map does not have to be known in advance. Furthermore, multiple robots are able to contribute to the same map and a previously recorded map is extendable when new areas are explored. This accounts for sensor noise or measurements which result from dynamic changes in the environment, e.g., because of dynamic objects. Modeling and updating is done in a probabilistic fashion. It is possible to add new information or sensor readings at any time.
OCCUPANCY GRID MAPPING UNKNOWN POSIES FREE
While the distinction between free and occupied space is essential for safe robot navigation, information about unknown areas is important, e.g., for autonomous exploration of an environment. If no information is available about an area (commonly denoted as unknown areas), this information is encoded as well. The representation models occupied areas as well as free space. The map is able to model arbitrary environments without prior assumptions about it. It is designed to meet the following requirements: It provides data structures and mapping algorithms. The OctoMap library implements a 3D occupancy grid mapping approach.
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