Floor plans are the basis of reasoning in and communicating about indoor environments. In this paper, we show that by modelling floor plans as sequences of line segments seen from a particular point of view, recent advances in autoregressive sequence modelling can be leveraged to model and predict floor plans. The line segments are canonicalized and translated to sequence of tokens and an attention-based neural network is used to fit a one-step distribution over next tokens. We fit the network to sequences derived from a set of large-scale floor plans, and demonstrate the capabilities of the model in four scenarios: novel floor plan generation, completion of partially observed floor plans, generation of floor plans from simulated sensor data, and finally, the applicability of a floor plan model in predicting the shortest distance with partial knowledge of the environment.

In this letter, 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(degrees) 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.

In map-predictive exploration planning, the aim is to exploit a-priori map information to improve planning for exploration in otherwise unknown environments. The use of map predictions in exploration planning leads to exacerbated greediness, as map predictions allow the planner to defer exploring parts of the environment that have low value, e.g., unfinished corners. This behavior is undesirable, as it leaves holes in the explored space by design. To this end, we propose a scoring function based on inverse covisibility that rewards visiting these low-value parts, resulting in a more cohesive exploration process, and preventing excessive greediness in a map-predictive setting. We examine the behavior of a non-greedy map-predictive planner in a bare-bones simulator, and answer two principal questions: a) how far beyond explored space should a map predictor predict to aid exploration, i.e., is more better; and b) does shortest-path search as the basis for planning, a popular choice, cause greediness. Finally, we show that by thresholding covisibility, the user can trade-off greediness for improved early exploration performance.

Autonomous exploration in mobile robotics is driven by two competing objectives: coverage, to exhaustively observe the environment; and path length, to do so with the shortest path possible. Though it is difficult to evaluate the best course of action without knowing the unknown, the unknown can often be understood through models, maps, or common sense. However, previous work has shown that improving estimates of information gain through such prior knowledge leads to greedy behavior and ultimately causes back- tracking, which degrades coverage performance. In fact, any information gain maximization will exhibit this behavior, even without prior knowledge. Information gained at task completion is constant, and cannot be maximized for. It is therefore an unsuitable choice as an optimization objective. Instead, information gain is a decision criterion for determining which candidate states should still be considered for exploration. The task therefore becomes to reach completion with the shortest total path. Since determining the shortest path is typically intractable, it is necessary to rely on a heuristic or estimate to identify candidate states that minimize the total path length. To address this, we propose a heuristic that reduces backtracking by preferring candidate states that are close to the robot, but far away from other candidate states. We evaluate the performance of the proposed heuristic in simulation against an information gain-based approach and frontier exploration, and show that our method significantly decreases total path length, both with and without prior knowledge of the environment.