An Efficient Sampling-based Method for Online Informative Path Planning in Unknown Environments

Abstract

The ability to plan informative paths online is essential to robot autonomy. In particular, sampling-based approaches are often used as they are capable of using arbitrary information gain formulations. However, they are prone to local minima, resulting in sub-optimal trajectories, and sometimes do not reach global coverage. In this paper, we present a new RRT*-inspired online informative path planning algorithm. Our method continuously expands a single tree of candidate trajectories and rewires segments to maintain the tree and refine intermediate trajectories. This allows the algorithm to achieve global coverage and maximize the utility of a path in a global context, using a single objective function. We demonstrate the algorithm's capabilities in the applications of autonomous indoor exploration as well as accurate Truncated Signed Distance Field (TSDF)-based 3D reconstruction on-board a Micro Aerial vehicle (MAV). We study the impact of commonly used information gain and cost formulations in these scenarios and propose a novel TSDF-based 3D reconstruction gain and cost-utility formulation. Detailed evaluation in realistic simulation environments show that our approach outperforms state of the art methods in these tasks. Experiments on a real MAV demonstrate the ability of our method to robustly plan in real-time, exploring an indoor environment solely with on-board sensing and computation. We make our framework available for future research.

8 pages, 6 figures, video: https://youtu.be/lEadqJ1_8Do, framework: https://github.com/ethz-asl/mav_active_3d_planning

References in corpus (3)

• Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning
• History-aware Autonomous Exploration in Confined Environments using MAVs
• Safe Local Exploration for Replanning in Cluttered Unknown Environments for Micro-Aerial Vehicles

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