Many scenarios require a robot to be able to explore its 3D environment online without human supervision. This is especially relevant for inspection tasks and search and rescue missions. To solve this high-dimensional path planning problem, sampling-based exploration algorithms have proven successful. However, these do not necessarily scale well to larger environments or spaces with narrow openings. This paper presents a 3D exploration planner based on the principles of Next-Best Views (NBVs). In this approach, a Micro-Aerial Vehicle (MAV)equipped with a limited field-of-view depth sensor randomly samples its configuration space to find promising future viewpoints. In order to obtain high sampling efficiency, our planner maintains and uses a history of visited places, and locally optimizes the robot's orientation with respect to unobserved space. We evaluate our method in several simulated scenarios, and compare it against a state-of-the-art exploration algorithm. The experiments show substantial improvements in exploration time (2 ⨯ faster), computation time, and path length, and advantages in handling difficult situations such as escaping dead-ends (up to 20 ⨯ faster). Finally, we validate the on-line capability of our algorithm on a computational constrained real world MAV.
The concept of NBVs was first introduced in @cite_27 , where the authors goal was to obtain a complete model of a scene by calculating a series of covering views. While not dealing explicitly with exploration, the idea of NBVs has been carried over into the exploration domain.
@cite_27
2096268286
There are situations in which one would like to know a good sequence of range-image views for obtaining a complete model of a scene. This paper describes two algorithms which use partial octree models to determine the "best" next view to take.