The use of Micro-Aerial Vehicles (MAVs) equipped with odometry- and depth sensors has become predominant for a wide variety of challenging industrial applications such as the autonomous exploration (i.e., digital mapping), and inspection (i.e., online surface reconstruction) of unknown facilities. However, despite the ongoing attention these topics receive, autonomous exploration systems still lack common evaluation grounds to assess their relative performance in terms of data and experimental tools. We address this deficit by introducing FLYBO, the first unified benchmark environment that focuses on the performance of such flying robots in terms of autonomous exploration and online surface reconstruction. It includes (i) 11 challenging realistic indoor- and outdoor datasets of increasing complexity and size, with ground-truth, (ii) a comprehensive benchmark of 7 of the top-performing autonomous exploration algorithms including methods without publicly available code. (iii) A unified experimental system factorizes the routines shared by autonomous planners in order to fairly and accurately assess their exploration performance in a controlled environment.

We address the problem of autonomous exploration of unknown environments using a Micro Aerial Vehicle (MAV) equipped with an active depth sensor. As such, the task consists in mapping the gradually discovered environment while planning the envisioned trajectories in real-time, using on-board computation only. To do so, we present SplatPlanner, an end-to-end autonomous planner that is based on a novel Permutohedral Frontier Filtering (PFF) which relies on a combination of highly efficient operations stemming from bilateral filtering using permutohedral lattices to guide the entire exploration. In particular, our PFF is computationally linear in input size, nearly parameter-free, and aggregates spatial information about frontier-neighborhoods into density scores in one single step. Comparative experiments made on simulated environments of increasing complexity show our method consistently outperforms recent state-of-the-art methods in terms of computational efficiency, exploration speed and qualitative coverage of scenes. Finally, we also display the practical capabilities of our end-to-end system in a challenging real-flight scenario.

In this paper, we address the problem of Multi-View Stereo (MVS) reconstruction of highly regular man-made scenes from calibrated, wide-baseline views and a sparse Structure-from-Motion (SfM) point cloud. We introduce a novel patch-based formulation via energy minimization which combines top-down segmentation hypotheses using appearance and vanishing line detections, as well as an arrangement of creased planar structures which are extracted automatically through a robust analysis of available SfM points and image features. The method produces a compact piecewise-planar depth map and a mesh which are aligned with the scene's structure. Experiments show that our approach not only reaches similar levels of accuracy w.r.t state-of-the-art pixel-based methods while using much fewer images, but also produces a much more compact, structure-aware mesh in a considerably shorter runtime by several of orders of magnitude.