Hypermap Mapping Framework and its Application to Autonomous Semantic Exploration
This addresses the burden of handling diverse map representations in robotic applications, though it appears incremental as it builds on existing mapping techniques.
The paper tackles the problem of managing multiple map types (occupancy, exploration, semantic) for autonomous robots by introducing the Hypermap framework, which can handle different map representations and includes an algorithm for generating semantic layers from RGB-D images, demonstrated through autonomous semantic exploration.
Modern intelligent and autonomous robotic applications often require robots to have more information about their environment than that provided by traditional occupancy grid maps. For example, a robot tasked to perform autonomous semantic exploration has to label objects in the environment it is traversing while autonomously navigating. To solve this task the robot needs to at least maintain an occupancy map of the environment for navigation, an exploration map keeping track of which areas have already been visited, and a semantic map where locations and labels of objects in the environment are recorded. As the number of maps required grows, an application has to know and handle different map representations, which can be a burden. We present the Hypermap framework, which can manage multiple maps of different types. In this work, we explore the capabilities of the framework to handle occupancy grid layers and semantic polygonal layers, but the framework can be extended with new layer types in the future. Additionally, we present an algorithm to automatically generate semantic layers from RGB-D images. We demonstrate the utility of the framework using the example of autonomous exploration for semantic mapping.