Method for Generating Semantic Grid Maps and Exploration Method Using Semantic Grid Maps

Semantic Grid Map Generation and Exploration Technology Using Door and Drop-off Area Indexing

This technology generates a semantic grid map by integrating semantic indices into existing occupancy grid maps based on environmental data acquired from laser scanners and downward-facing distance sensors. It then utilizes this map to extract boundaries of unknown areas and plan exploration paths.

Conventional occupancy grid maps can only identify the presence of obstacles and fail to distinguish between specific types, such as doors or drop-off areas, making it difficult to ensure driving safety. Furthermore, these maps suffer from high memory overhead during mapping and exploration, as well as risks in path planning.

This technology identifies door features by extracting line segments from environmental data and detects drop-off areas using downward-facing distance sensors, classifying them with semantic indices. It also proposes a method for setting exploration candidate nodes by clustering the boundaries between unknown and known areas. It can be applied to indoor autonomous exploration robots to prevent accidents by proactively identifying hazards such as stairs or cliffs.

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Key Features:

• A sensing step of acquiring environmental information of a target area from a sensing unit including a laser scanner
• A step of extracting features based on the environmental information and generating a semantic grid map indicated by semantic indices
• A step of adjusting the occupancy probability for the grids of the semantic grid map based on the environmental information
• A configuration for setting exploration candidate nodes and generating paths by clustering the boundaries of unknown areas