Beyond the Frontier: Predicting Unseen Walls from Occupancy Grids by Learning from Floor Plans
Ludvig Ericson, Patric Jensfelt
In this paper, we tackle the challenge of predicting the unseen walls of
a partially observed environment as a set of 2D line segments,
conditioned on occupancy grids integrated along the trajectory of a 360°
LIDAR sensor. A dataset of such occupancy grids and their corresponding
target wall segments is collected by navigating a virtual robot between a
set of randomly sampled waypoints in a collection of office-scale floor
plans from a university campus. The line segment prediction task is
formulated as an autoregressive sequence prediction task, and an
attention- based deep network is trained on the dataset. The sequence-
based autoregressive formulation is evaluated through predicted
information gain, as in frontier-based autonomous exploration,
demonstrating significant improvements over both non-predictive
estimation and convolution-based image prediction found in the
literature. Ablations on key components are evaluated, as well as sensor
range and the occupancy grid’s metric area. Finally, model generality is
validated by predicting walls in a novel floor plan reconstructed
on-the-fly in a real-world office environment.
