BoxMap: Efficient Structural Mapping and Navigation

While humans can successfully navigate using abstractions, ignoring details that are irrelevant to the task at hand, most of the existing approaches in robotics require detailed environment representations which consume a significant amount of sensing, computing, and storage; these issues become particularly important in resource-constrained settings with limited power budgets. Deep learning methods can learn from prior experience to abstract knowledge from novel environments, and use it to more efficiently execute tasks such as frontier exploration, object search, or scene understanding. 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 (rooms and doors) and their relations (connectivity). The predictions from low-level measurements can be leveraged to achieve high-level goals with lower computational costs than methods based on detailed representations. As an example application, we consider a robot equipped with a 2-D laser scanner tasked with exploring a residential building. Our BoxMap representation scales quadratically with the number of rooms (with a small constant), resulting in significant savings over a full geometric map. Moreover, our high-level topological representation results in 30. 9 % shorter trajectories in the exploration task with respect to a standard method. Code is available at: bit. ly/3F6w2Yl.

Authors

• Zili Wang
• Christopher Allum
• Sean B. Andersson
• Roberto Tron

Keywords

• Training
• Adaptation models
• Navigation
• Semantics
• Computer architecture
• Robot sensing systems
• Sensors
• Trajectory
• Standards
• Testing
• Deep Learning
• Detailed Representation
• Graph Topology
• Short Trajectories
• Exploration Task
• Robotic Approach
• Geometric Function
• Convolutional Neural Network
• Centroid
• Image Features
• Prediction Error
• Complex Environment
• Recurrent Neural Network
• Bounding Box
• Final Loss
• Encoder Layer
• Unknown Environment
• Decoder Layer
• Simultaneous Localization And Mapping
• Robot Operating System
• Occupancy Map
• Convolutional Neural Networks Backbone
• Prediction Head
• Structural Similarity Index Measure
• Box Coordinates
• Exploration Algorithm
• Laser Measurement