Wireframe Mapping for Resource-Constrained Robots

This paper presents a novel wireframe map structure for resource-constrained robots operating in a rectilinear 2D environment. The wireframe representation compactly represents geometry, in addition to transient situations such as occlusions and boundaries of unexplored regions. We formulate a particle filter to suit this sparse wireframe map structure. Functions for calculating the likelihood of scans, merging wireframes, and resampling are developed to accommodate this map representation. The wireframe structure with the particle filter allows for severe discrete map errors to be corrected, leading to accurate maps with small storage requirements. We show in a simulation study that the algorithm attains a map of an environment with 1% error, compared to an occupancy grid map obtained with GMapping which attained 23% error with the same storage requirements. A simulation mapping a large environment demonstrates the algorithms scalability.