BoxMap: Efficient Structural Mapping and Navigation
Zili Wang, Christopher Allum, Sean B. Andersson, and Roberto Tron

TL;DR
BoxMap introduces a novel detection-transformer architecture that efficiently creates a topological map of environments, enabling resource-constrained robots to explore and navigate more efficiently by abstracting environment structure.
Contribution
The paper presents a new deep learning-based topological mapping method that reduces computational costs by abstracting environment structure into semantic entities and relations.
Findings
Quadratic scaling of map with number of rooms
30.9% shorter exploration trajectories
Significant savings over detailed geometric maps
Abstract
While humans can successfully navigate using abstractions, ignoring details that are irrelevant to the task at hand, most existing robotic applications require the maintenance of a detailed environment representation which consumes a significant amount of sensing, computing, and storage. These issues are particularly important in a resource-constrained setting with limited power budget. Deep learning methods can learn from prior experience to abstract knowledge of unknown environments, and use it to execute tasks (e.g., frontier exploration, object search, or scene understanding) more efficiently. We propose BoxMap, a Detection-Transformer-based architecture that takes advantage of the structure of the sensed partial environment to update a topological graph of the environment as a set of semantic entities (e.g. rooms and doors) and their relations (e.g. connectivity). These predictions…
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Taxonomy
TopicsGeographic Information Systems Studies · Data Management and Algorithms · Constraint Satisfaction and Optimization
